Rotating equipment in a petrochemical plant or refinery

ABSTRACT

A plant or refinery may include equipment such as condensers, regenerators, distillation columns, rotating equipment, compressors, pumps, turbines, or the like. Different operating methods may impact deterioration in equipment condition, thereby prolonging equipment life, extending production operating time, or providing other benefits. Mechanical or digital sensors may be used for monitoring equipment to determine whether problems are developing. For example, sensors may be used in conjunction with one or more system components to perform invariant mapping, monitor system operating characteristics, and/or predict pressure, volume, surges, reactor loop fouling, gas quality, or the like. An operating condition (e.g., of one or more pieces of equipment in the plant or refinery) may be adjusted to prolong equipment life or avoid equipment failure.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 62/477,899, filed Mar. 28, 2017, which is incorporated by reference in its entirety.

FIELD

The present disclosure is related to a method and system for improving the performance of components that make up operations in a plant, such as a carbonaceous processing plant, a chemical plant, a petrochemical plant, or a refinery. Typical plants may be those that provide catalytic dehydrogenation or hydrocarbon cracking.

BACKGROUND

A plant or refinery may include one or more pieces of equipment for performing a process. Equipment may break down over time, and need to be repaired or replaced. Additionally, a process may be more or less efficient depending on one or more operating characteristics. There will always be a need for improving process efficiencies and improving equipment reliability.

SUMMARY

The following summary presents a simplified summary of certain features. The summary is not an extensive overview and is not intended to identify key or critical elements.

One or more embodiments may include a system including a reactor; a heater; a condenser; a rotating equipment; a valve associated with the rotating equipment; one or more sensors associated with the rotating equipment; a data collection platform; and a data analysis platform. The data collection platform may include one or more processors of the data collection platform; a communication interface of the data collection platform; and memory storing executable instructions that, when executed, cause the data collection platform to: receive, from the one or more sensors associated with the rotating equipment, sensor data including operation information associated with the rotating equipment; correlate the sensor data from the one or more sensors with metadata including time data, the time data corresponding to the operation information associated with the rotating equipment; and transmit the sensor data. The data analysis platform may include one or more processors of the data analysis platform; a communication interface of the data analysis platform; and memory storing executable instructions that, when executed, cause the data analysis platform to: receive, from the data collection platform, the sensor data including the operation information associated with the rotating equipment; analyze the sensor data including the operation information associated with the rotating equipment; based on analyzing the sensor data including the operation information associated with the rotating equipment, determine an operating condition of the rotating equipment; based on the operating condition of the rotating equipment, determine an adjustment to the operating condition of the rotating equipment; and send a command configured to cause the adjustment to the operating condition of the rotating equipment by causing adjustment to the valve associated with the rotating equipment.

One or more embodiments may include a method of operation for a plant including a reactor, a heater, a condenser, a rotating equipment, and a valve associated with the rotating equipment. The method may include, at a data analysis platform including one or more processors, memory, and a communication interface in communication with one or more sensors associated with the rotating equipment: receiving sensor data including operation information associated with the rotating equipment; analyzing the sensor data including the operation information associated with the rotating equipment; based on analyzing the sensor data including the operation information associated with the rotating equipment, determining an operating condition of the rotating equipment; based on the operating condition of the rotating equipment, determining an adjustment to the operating condition of the rotating equipment; and sending a command configured to cause the adjustment to the operating condition of the rotating equipment by causing adjustment to the valve associated with the rotating equipment.

One or more embodiments may include a method of operation for a refinery including a reactor, a heater, a condenser, a rotating equipment, and a valve associated with the rotating equipment. The method may include, at a data analysis platform including one or more processors, memory, and a communication interface in communication with one or more sensors associated with the rotating equipment: receiving sensor data including operation information associated with the rotating equipment; analyzing the sensor data including the operation information associated with the rotating equipment; based on analyzing the sensor data including the operation information associated with the rotating equipment, determining an operating condition of the rotating equipment; based on the operating condition of the rotating equipment, determining an adjustment to the operating condition of the rotating equipment; and sending a command configured to cause the adjustment to the operating condition of the rotating equipment by causing adjustment to the valve associated with the rotating equipment.

One or more embodiments may include one or more non-transitory computer readable media storing executable instructions that, when executed by one or more processors, cause a system including a reactor; a heater; a condenser; a rotating equipment; a valve associated with the rotating equipment; and one or more sensors associated with the rotating equipment to: receive, from the one or more sensors associated with the rotating equipment, sensor data including operation information associated with the rotating equipment; correlate the sensor data from the one or more sensors with metadata including time data corresponding to the operation information associated with the rotating equipment; transmit the sensor data; receive the sensor data including the operation information associated with the rotating equipment; analyze the sensor data including the operation information associated with the rotating equipment; based on analyzing the sensor data including the operation information associated with the rotating equipment, determine an operating condition of the rotating equipment; based on the operating condition of the rotating equipment, determine an adjustment to the operating condition of the rotating equipment; and send a command configured to cause the adjustment to the operating condition of the rotating equipment by causing adjustment to the valve associated with the rotating equipment.

One or more embodiments may include a system that includes a reactor; a heater; a system control valve; a compressor; one or more sensors associated with the compressor, the one or more sensors comprising a pressure sensor associated with a head end of the compressor or a crank end of the compressor; a data collection platform; and/or a data analysis platform. The data collection platform may include one or more processors of the data collection platform; a communication interface of the data collection platform; and memory storing executable instructions that, when executed, cause the data collection platform to: receive, from the one or more sensors associated with the compressor, sensor data associated with the compressor and collected by the one or more sensors associated with the compressor; and send the sensor data associated with the compressor and collected by the one or more sensors associated with the compressor. The data analysis platform may include one or more processors of the data analysis platform; a communication interface of the data analysis platform; and computer-readable memory storing executable instructions that, when executed, cause the data analysis platform to: receive the sensor data associated with the compressor and collected by the one or more sensors associated with the compressor, the sensor data comprising information about a pressure and volume compression cycle within the compressor; analyze the sensor data associated with the compressor to predict a part failure of the compressor based on the information about the pressure and volume compression cycle within the compressor; and based on predicting the part failure of the compressor, send a command configured to cause adjustment of the system control valve to reduce a pressure associated with the compressor.

One or more embodiments may include one or more non-transitory computer-readable media storing executable instructions that, when executed, cause a system to: receive, by a data analysis computing device, sensor data associated with a compressor and collected by one or more sensors associated with the compressor, the sensor data comprising information about a pressure and volume compression cycle within the compressor; analyze the sensor data associated with the compressor to predict a part failure of the compressor based on the information about the pressure and volume compression cycle within the compressor; and based on predicting the part failure of the compressor, send a command configured to cause adjustment of a system control valve associated with the compressor to reduce a pressure associated with the compressor.

One or more embodiments may include a method that includes receiving, by a data analysis computing device, sensor data associated with a compressor and collected by one or more sensors associated with the compressor, the sensor data comprising information about a pressure and volume compression cycle within the compressor; analyzing, by the data analysis computing device, the sensor data associated with the compressor to predict a part failure of the compressor based on the information about the pressure and volume compression cycle within the compressor; and based on predicting the part failure of the compressor, sending, by the data analysis computing device, a command configured to cause adjustment of a system control valve associated with the compressor to reduce a pressure associated with the compressor.

One or more embodiments may include a system including a reactor; a heater; a system control valve; a compressor; one or more sensors associated with the compressor; a data collection platform, and/or a data analysis platform. The data collection platform may include one or more processors of the data collection platform; a communication interface of the data collection platform; and computer-readable memory storing executable instructions that, when executed, cause the data analysis platform to: receive, from the one or more sensors associated with the compressor, a stream of sensor data associated with the compressor and collected by the one or more sensors associated with the compressor; and send the stream of sensor data associated with the compressor and collected by the one or more sensors associated with the compressor. The data analysis platform may include one or more processors of the data analysis platform; a communication interface of the data analysis platform; and computer-readable memory storing executable instructions that, when executed, cause the data analysis platform to: receive the stream of sensor data associated with the compressor and collected by the one or more sensors associated with the compressor; analyze the stream of sensor data associated with the compressor to determine a potential surge event within the compressor; and based on determining the potential surge event within the compressor, send a command configured to cause adjustment of the system control valve to reduce load on the compressor.

One or more embodiments may include One or more non-transitory computer-readable media storing executable instructions that, when executed, cause a data analysis platform to: receive sensor data associated with a compressor and collected by one or more sensors associated with the compressor; analyze the sensor data associated with the compressor to determine a potential surge event within the compressor; and based on determining the potential surge event within the compressor, send a command configured to cause adjustment of a system control valve associated with the compressor to reduce load on the compressor.

One or more embodiments may include a method that includes receiving, by a data analysis computing device, sensor data associated with a compressor and collected by one or more sensors associated with the compressor; analyzing, by the data analysis computing device, the sensor data associated with the compressor to determine a potential surge event within the compressor; and based on determining the potential surge event within the compressor, sending, by the data analysis computing device, a command configured to cause adjustment of a system control valve associated with the compressor to reduce load on the compressor.

One or more embodiments may include a system that includes a reactor; a heater; a compressor comprising one or more injection ports; one or more sensors associated with the compressor; a data collection platform, and/or a data analysis platform. The data collection platform may include one or more processors of the data collection platform; a communication interface of the data collection platform; and computer-readable memory storing executable instructions that, when executed, cause the data analysis platform to: receive, from the one or more sensors associated with the compressor, sensor data associated with the compressor and collected by the one or more sensors associated with the compressor; and send the sensor data associated with the compressor and collected by the one or more sensors associated with the compressor. The data analysis platform may include one or more processors of the data analysis platform; a communication interface of the data analysis platform; and computer-readable memory storing executable instructions that, when executed, cause the data analysis platform to: receive the sensor data associated with the compressor and collected by the one or more sensors associated with the compressor; analyze the sensor data associated with the compressor to determine potential fouling within the compressor; and based on determining the potential fouling within the compressor, send a command configured to cause an online wash via the one or more injection ports of the compressor to reduce the potential fouling within the compressor.

One or more embodiments may include one or more non-transitory computer-readable media storing executable instructions that, when executed, cause a data analysis platform to: receive sensor data associated with a compressor comprising one or more injection ports and collected by one or more sensors associated with the compressor; analyze the sensor data associated with the compressor to determine potential fouling within the compressor; and based on determining the potential fouling within the compressor, send a command configured to cause an online wash via the one or more injection ports of the compressor to reduce the potential fouling within the compressor.

One or more embodiments may include a method including receiving, by a data analysis computing device, sensor data associated with a compressor comprising one or more injection ports and collected by one or more sensors associated with the compressor; analyzing, by the data analysis computing device, the sensor data associated with the compressor to determine potential fouling within the compressor; and based on determining the potential fouling within the compressor, sending, by the data analysis computing device, a command configured to cause an online wash via the one or more injection ports of the compressor to reduce the potential fouling within the compressor.

One or more embodiments may include a system that includes a reactor; a heater; a compressor; a valve associated with the compressor; one or more sensors associated with the compressor, the one or more sensors comprising a temperature sensor; a data collection platform, and a data analysis platform. The data collection platform may include one or more processors of the data collection platform; a communication interface of the data collection platform; and memory storing executable instructions that, when executed, cause the data collection platform to: receive, from the one or more sensors associated with the compressor, sensor data comprising operation information associated with the compressor; correlate the sensor data with metadata comprising time data, the time data corresponding to the operation information associated with the compressor; and transmit the sensor data. The data analysis platform may include one or more processors of the data analysis platform; a communication interface of the data analysis platform; and computer-readable memory storing executable instructions that, when executed, cause the data analysis platform to: receive the sensor data comprising the operation information associated with the compressor; analyze the sensor data to determine an increase in temperature downstream of the valve associated with the compressor; and based on determining the increase in temperature downstream of the valve associated with the compressor, send a command configured to cause re-seating of the valve by opening the valve and closing the valve.

One or more embodiments may include One or more non-transitory computer-readable media storing executable instructions that, when executed, cause a data analysis platform to: receive sensor data associated with a compressor and collected by one or more sensors associated with the compressor; analyze the sensor data associated with the compressor to determine an increase in temperature downstream of a valve associated with the compressor; and based on determining the increase in temperature downstream of the valve associated with the compressor, send a command configured to cause re-seating of the valve by opening the valve and closing the valve.

One or more embodiments may include a method that includes receiving, by a data analysis computing device, sensor data associated with a compressor and collected by one or more sensors associated with the compressor; analyzing, by the data analysis computing device, the sensor data associated with the compressor to determine an increase in temperature downstream of a valve associated with the compressor; and based on determining the increase in temperature downstream of the valve associated with the compressor, sending, by the data analysis computing device, a command configured to cause re-seating of the valve by opening the valve and closing the valve.

One or more embodiments may include a system that includes a reactor; a heater; a centrifugal compressor comprising a dry gas seal; one or more sensors associated with the centrifugal compressor; a data collection platform, and/or a data analysis platform. The data collection platform may include one or more processors of the data collection platform; a communication interface of the data collection platform; and computer-readable memory storing executable instructions that, when executed, cause the data collection platform to: receive, from the one or more sensors associated with the centrifugal compressor, sensor data associated with the centrifugal compressor and collected by the one or more sensors associated with the centrifugal compressor; and send the sensor data associated with the centrifugal compressor and collected by the one or more sensors associated with the centrifugal compressor. The data analysis platform may include one or more processors of the data analysis platform; a communication interface of the data analysis platform; and computer-readable memory storing executable instructions that, when executed, cause the data analysis platform to: receive the sensor data associated with the centrifugal compressor and collected by the one or more sensors associated with the centrifugal compressor; analyze the sensor data associated with the centrifugal compressor to determine a composition of gas used in the centrifugal compressor; determine, based on the composition of the gas used in the centrifugal compressor, a potential component failure of the centrifugal compressor; and based on determining the potential component failure of the centrifugal compressor, send a command configured to cause an adjustment to an operating parameter of the centrifugal compressor.

One or more embodiments may include one or more non-transitory computer-readable media storing executable instructions that, when executed, cause a data analysis platform to: receive sensor data associated with a centrifugal compressor and collected by one or more sensors associated with the centrifugal compressor; analyze the sensor data associated with the centrifugal compressor to determine a composition of gas used in the centrifugal compressor; determine, based on the composition of the gas used in the centrifugal compressor, a potential component failure of the centrifugal compressor; and based on determining the potential component failure of the centrifugal compressor, send a command configured to cause an adjustment to an operating parameter of the centrifugal compressor.

One or more embodiments may include a method that includes receiving, by a data analysis computing device, sensor data associated with a centrifugal compressor and collected by one or more sensors associated with the centrifugal compressor; analyzing, by the data analysis computing device, the sensor data associated with the centrifugal compressor to determine a composition of gas used in the centrifugal compressor; determining, by the data analysis computing device and based on the composition of the gas used in the centrifugal compressor, a potential component failure of the centrifugal compressor; and based on determining the potential component failure of the centrifugal compressor, sending, by the data analysis computing device, a command configured to cause an adjustment to an operating parameter of the centrifugal compressor.

One or more embodiments may include a system that includes a reactor; a heater; a reciprocating compressor comprising a cylinder; one or more sensors associated with the reciprocating compressor and configured to collect sensor data associated with the reciprocating compressor; a data collection platform, and/or a data analysis platform. The data collection platform may include one or more processors of the data collection platform; a communication interface of the data collection platform; and memory storing executable instructions that, when executed, cause the data collection platform to: collect, from the one or more sensors associated with the reciprocating compressor, the sensor data associated with the reciprocating compressor; and send the sensor data associated with the reciprocating compressor. The data analysis platform may include one or more processors of the data analysis platform; a communication interface of the data analysis platform; and memory storing executable instructions that, when executed, cause the data analysis platform to: receive the sensor data associated with the reciprocating compressor; analyze the sensor data associated with the reciprocating compressor to detect condensation in the reciprocating compressor; and based on detecting the condensation in the reciprocating compressor, send a command configured to cause an adjustment to an operating parameter of the reciprocating compressor.

One or more embodiments may include one or more non-transitory computer-readable media storing executable instructions that, when executed, cause a data analysis platform to: receive sensor data associated with a reciprocating compressor and collected by one or more sensors associated with the reciprocating compressor; analyze the sensor data associated with the reciprocating compressor to detect condensation in the reciprocating compressor; and based on detecting the condensation in the reciprocating compressor, send a command configured to cause an adjustment to an operating parameter of the reciprocating compressor.

One or more embodiments may include a method that includes receiving, by a data analysis computing device, sensor data associated with a reciprocating compressor and collected by one or more sensors associated with the reciprocating compressor; analyzing, by the data analysis computing device, the sensor data associated with the reciprocating compressor to detect condensation in the reciprocating compressor; and based on detecting the condensation in the reciprocating compressor, sending, by the data analysis computing device, a command configured to cause an adjustment to an operating parameter of the reciprocating compressor.

One or more embodiments may include a system that includes a reactor; a heater; a centrifugal compressor; one or more sensors associated with the centrifugal compressor; a data collection platform, and/or a data analysis platform. The data collection platform may include one or more processors of the data collection platform; a communication interface of the data collection platform; and memory storing executable instructions that, when executed, cause the data collection platform to: collect sensor data from the one or more sensors associated with the centrifugal compressor; and send the sensor data from the one or more sensors associated with the centrifugal compressor. The data analysis platform may include one or more processors of the data analysis platform; a communication interface of the data analysis platform; and memory storing executable instructions that, when executed, cause the data analysis platform to: receive sensor data associated with the centrifugal compressor and collected by the one or more sensors associated with the centrifugal compressor; analyze the sensor data associated with the centrifugal compressor to determine a molecular weight of gas flow of gas in the centrifugal compressor; use the molecular weight of the gas flow of the gas in the centrifugal compressor to evaluate a performance of the centrifugal compressor; determine a recommended action to improve the performance of the centrifugal compressor; and send an alert including information regarding the performance of the centrifugal compressor and the recommended action to improve the performance of the centrifugal compressor.

One or more embodiments may include one or more non-transitory computer-readable media storing executable instructions that, when executed, cause a data analysis platform to: receive sensor data associated with a centrifugal compressor and collected by one or more sensors associated with the centrifugal compressor; analyze the sensor data associated with the centrifugal compressor to determine a molecular weight of gas flow of gas in the centrifugal compressor; use the molecular weight of the gas flow of the gas in the centrifugal compressor to evaluate a performance of the centrifugal compressor; determine a recommended action to improve the performance of the centrifugal compressor; and send an alert including information regarding the performance of the centrifugal compressor and the recommended action to improve the performance of the centrifugal compressor.

One or more embodiments may include a method that includes receiving, by a data analysis computing device, sensor data associated with a centrifugal compressor and collected by one or more sensors associated with the centrifugal compressor; analyzing, by the data analysis computing device, the sensor data associated with the centrifugal compressor to determine a molecular weight of gas flow of gas in the centrifugal compressor; using, by the data analysis computing device, the molecular weight of the gas flow of the gas in the centrifugal compressor to evaluate a performance of the centrifugal compressor; determining, by the data analysis computing device, a recommended action to improve the performance of the centrifugal compressor; and sending, by the data analysis computing device, an alert including information regarding the performance of the centrifugal compressor and the recommended action to improve the performance of the centrifugal compressor.

Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF DRAWINGS

The present disclosure is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which:

FIG. 1A depicts an illustrative arrangement for a catalytic dehydrogenation process in accordance with one or more example embodiments;

FIG. 1B depicts an illustrative arrangement for a fluid catalytic cracking process in accordance with one or more example embodiments;

FIG. 2 depicts an illustrative arrangement for a centrifugal compressor configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 3 depicts an illustrative arrangement for an axial compressor configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 4 depicts a graph illustrating typical performance operation of a centrifugal or axial compressor in accordance with one or more example embodiments;

FIG. 5A depicts an illustrative arrangement for a portion of a reciprocating compressor configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 5B depicts an illustrative arrangement for a reciprocating compressor configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 6 depicts an illustrative arrangement for a steam turbine configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 7A depicts an illustrative arrangement for a reciprocating compressor with pressure and crank angle sensors configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 7B depicts an illustrative arrangement for a reciprocating compressor with vibration sensors configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 7C depicts an illustrative arrangement for a reciprocating compressor with a temperature sensor configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 8A depicts an illustrative arrangement for a centrifugal compressor with pressure, temperature, flow, and speed sensors configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 8B depicts a graph illustrating typical performance operation of a centrifugal or axial compressor, along with control lines used by a computer system in taking action with respect to the performance of the compressor;

FIG. 8C depicts a graph illustrating typical surge behavior of a centrifugal or axial compressor in accordance with one or more example embodiments;

FIG. 9A depicts an illustrative arrangement for a centrifugal compressor with pressure, temperature, and vibration sensors configured for use in connection with the arrangement of FIG. 1 in accordance with one or more example embodiments;

FIG. 9B depicts an illustrative arrangement for a centrifugal compressor with wash injection ports configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 10A depicts an illustrative arrangement for a reciprocating compressor within a petrochemical plant, configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 10B depicts an illustrative arrangement for a reciprocating compressor within a petrochemical plant, illustrating the positioning of certain valves and sensors, configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 10C depicts an illustrative arrangement for a spring hanger pipe support in accordance with one or more example embodiments;

FIG. 10D depicts an illustrative arrangement for a centrifugal pump with pressure and temperature sensors on a seal gland, in accordance with one or more example embodiments;

FIG. 10E depicts an illustrative arrangement for a centrifugal pump within a petrochemical plant, in accordance with one or more example embodiments;

FIG. 10F depicts an illustrative arrangement for a centrifugal pump with a pressure sensor on the balance line, in accordance with one or more example embodiments;

FIG. 10G depicts an illustrative arrangement for a centrifugal pump with temperature sensors on the seal flush line cooler, in accordance with one or more example embodiments;

FIG. 10H depicts an illustrative arrangement for a centrifugal compressor with pressure and temperature sensors configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 10I depicts an illustrative arrangement for a centrifugal compressor with pressure, flow, temperature, and power sensors configured for use in connection with the arrangement of FIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 11A depicts an illustrative computing environment for managing the operation of one or more pieces of equipment in a plant in accordance with one or more example embodiments;

FIG. 11B depicts an illustrative data collection computing platform for collecting data related to the operation of one or more pieces of equipment in a plant in accordance with one or more example embodiments;

FIG. 11C depicts an illustrative data analysis computing platform for analyzing data related to the operation of one or more pieces of equipment in a plant in accordance with one or more example embodiments;

FIG. 11D depicts an illustrative data analysis computing platform for analyzing data related to the operation of one or more pieces of equipment in a plant in accordance with one or more example embodiments;

FIG. 11E depicts an illustrative control computing platform for controlling one or more parts of one or more pieces of equipment in a plant in accordance with one or more example embodiments;

FIGS. 12A-12B depict an illustrative flow diagram of one or more steps that one more devices may perform in controlling one or more aspects of a plant operation in accordance with one or more example embodiments;

FIGS. 13-14 depict illustrative graphical user interfaces related to one or more aspects of a plant operation in accordance with one or more example embodiments; and

FIG. 15 depicts an illustrative flowchart of a process that one or more devices may perform in controlling one or more aspects of a plant operation in accordance with one or more example embodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown, by way of illustration, various embodiments in which aspects of the disclosure may be practiced. It is to be understood that other embodiments may be used, and structural and functional modifications may be made, without departing from the scope of the present disclosure.

It is noted that various connections between elements are discussed in the following description. It is noted that these connections are general and, unless specified otherwise, may be direct or indirect, wired or wireless, and that the specification is not intended to be limiting in this respect.

A chemical plant or a petrochemical plant or a refinery may include one or more pieces of equipment that process one or more input chemicals to create one or more products. For example, catalytic dehydrogenation can be used to convert paraffins to the corresponding olefin, e.g., propane to propene, or butane to butene.

A multitude of process equipment may be used in the chemical, refining, and petrochemical industry including, but not limited to, slide valves, rotating equipment, pumps, compressors, heat exchangers, fired heaters, control valves, fractionation columns, reactors, and/or shut-off valves.

Elements of chemical and petrochemical/refinery plants may be exposed to the outside and thus can be exposed to various environmental stresses. Such stresses may be weather related, such as temperature extremes (hot and cold), high-wind conditions, and precipitation conditions such as snow, ice, and rain. Other environmental conditions may be pollution particulates, such as dust and pollen, or salt if located near an ocean, for example. Such stresses can affect the performance and lifetime of equipment in the plants. Different locations may have different environmental stresses. For example, a refinery in Texas may have different stresses than a chemical plant in Montana.

Process equipment may deteriorate over time, affecting the performance and integrity of the process. Such deteriorating equipment may ultimately fail, but before failing, may decrease efficiency, yield, and/or product properties. It is desirable that corrective actions be taken in advance of equipment inefficiencies and/or failure.

FIG. 1A shows one typical arrangement for a catalytic dehydrogenation process 5. The process 5 includes a reactor section 10, a catalyst regeneration section 15, and a product recovery section 20.

The reactor section 10 includes one or more reactors 25. A hydrocarbon feed 30 is sent to a heat exchanger 35 where it exchanges heat with a reactor effluent 40 to raise the feed temperature. The feed 30 is sent to a preheater 45 where it is heated to the desired inlet temperature. The preheated feed 50 is sent from the preheater 45 to the first reactor 25. Because the dehydrogenation reaction is endothermic, the temperature of the effluent 55 from the first reactor 25 is less than the temperature of the preheated feed 50. The effluent 55 is sent to interstage heaters 60 to raise the temperature to the desired inlet temperature for the next reactor 25.

After the last reactor, the reactor effluent 40 is sent to the heat exchanger 35, and heat is exchanged with the feed 30. The reactor effluent 40 is then sent to the product recovery section 20. The catalyst 65 moves through the series of reactors 25. When the catalyst 70 leaves the last reactor 25, it is sent to the catalyst regeneration section 15. The catalyst regeneration section 15 includes a regenerator 75 where coke on the catalyst is burned off and the catalyst may go through a reconditioning step. A regenerated catalyst 80 is sent back to the first reactor 25.

The reactor effluent 40 is compressed in the compressor or centrifugal compressor 82. The compressed effluent 115 is introduced to a cooler 120, for instance a heat exchanger. The cooler 120 lowers the temperature of the compressed effluent. The cooled effluent 125 (cooled product stream) is then introduced into a chloride remover 130, such as a chloride scavenging guard bed. The chloride remover 130 includes an adsorbent, which adsorbs chlorides from the cooled effluent 125 and provides a treated effluent 135. Treated effluent 135 is introduced to a drier 84.

The dried effluent is separated in separator 85. Gas 90 is expanded in expander 95 and separated into a recycle hydrogen stream 100 and a net separator gas stream 105. A liquid stream 110, which includes the olefin product and unconverted paraffin, is sent for further processing, where the desired olefin product is recovered and the unconverted paraffin is recycled to the dehydrogenation reactor 25.

FIG. 1B shows a typical fluid catalytic cracking (FCC) process, which includes an FCC fluidized bed reactor and a spent catalyst regenerator. Regenerated cracking catalyst entering the reactor, from the spent catalyst regenerator, is contacted with an FCC feed stream in a riser section at the bottom of the FCC reactor, to catalytically crack the FCC feed stream and provide a product gas stream, including cracked hydrocarbons having a reduced molecular weight, on average, relative to the average molecular weight of feed hydrocarbons in the FCC feed stream. As shown in FIG. 1B, steam and lift gas are used as carrier gases that upwardly entrain the regenerated catalyst in the riser section, as it contacts the FCC feed. In this riser section, heat from the catalyst vaporizes the FCC feed stream, and contact between the catalyst and the FCC feed causes cracking of this feed to lower molecular weight hydrocarbons, as both the catalyst and feed are transferred up the riser and into the reactor vessel. A product gas stream including the cracked (e.g., lower molecular weight) hydrocarbons may be separated from spent cracking catalyst at or near the top of the reactor vessel, preferably using internal solid/vapor separation equipment, such as cyclone separators. This product gas stream, essentially free of spent cracking catalyst, then exits the reactor vessel through a product outlet line for further transport to the downstream product recovery section.

The spent or coked catalyst, following its disengagement or separation from the product gas stream, requires regeneration for further use. This coked catalyst first falls into a dense bed stripping section of the FCC reactor, into which steam is injected, through a nozzle and distributor, to purge any residual hydrocarbon vapors that would be detrimental to the operation of the regenerator. After this purging or stripping operation, the coked catalyst is fed by gravity to the catalyst regenerator through a spent catalyst standpipe. FIG. 1B depicts a regenerator, which can also be referred to as a combustor. Various configurations of regenerators may be used. In the spent catalyst regenerator, a stream of oxygen-containing gas, such as air, is introduced to contact the coked catalyst, burn coke deposited thereon, and provide regenerated catalyst, having most or all of its initial coke content converted to combustion products, including CO2, CO, and H2O vapors that exit in a flue gas stream. The regenerator operates with catalyst and the oxygen-containing gas (e.g., air) flowing upwardly together in a combustor riser that is located within the catalyst regenerator. At or near the top of the regenerator, following combustion of the catalyst coke, regenerated cracking catalyst is separated from the flue gas using internal solid/vapor separation equipment (e.g., cyclones) to promote efficient disengagement between the solid and vapor phases.

In the FCC recovery section, the product gas stream exiting the FCC reactor is fed to a bottom section of an FCC main fractionation column. Several product fractions may be separated on the basis of their relative volatilities and recovered from this main fractionation column. Representative product fractions include, for example, naphtha (or FCC gasoline), light cycle oil, and heavy cycle oil.

Other petrochemical processes produce desirable products, such as turbine fuel, diesel fuel and other products referred to as middle distillates, as well as lower boiling hydrocarbonaceous liquids, such as naphtha and gasoline, by hydrocracking a hydrocarbon feedstock derived from crude oil or heavy fractions thereof. Feedstocks most often subjected to hydrocracking are the gas oils and heavy gas oils recovered from crude oil by distillation. For example, the conversion of methanol to olefins (MTO) produces ethylene and propylene from natural gas or coal. MTO enables low costs of production for ethylene and propylene and produces olefins at high ratios of propylene to ethylene than other processes. Rapid thermal processing (RTP) (Ensyn's patented RTP® technology) uses renewable cellulosic biomass, typically wood-derived feedstocks, in a thermal conversion process that produces high yields of free-flowing liquid biocrude. The technology uses a process similar to the FCC process but circulates an inert sand heat carrier, instead of catalyst, to convert the biomass to a biocrude.

References herein to a “plant” are to be understood to refer to any of various types of chemical and petrochemical manufacturing or refining facilities. References herein to a plant “operators” are to be understood to refer to and/or include, without limitation, plant planners, managers, engineers, technicians, operators, and others interested in, overseeing, and/or running the daily operations at a plant.

Rotating Equipment Technology

A system or arrangement as described above may include various compressors, pumps, and/or turbines, and FIGS. 1A and 1B illustrate example locations for some of such components, which may be additionally or alternately used in other locations. Compressors may be used to compress gases within the system (e.g., a reactor effluent) to provide a compressed gas. Compression includes increasing a pressure of the gas and may also change other properties such as temperature. Pumps may be used to force fluids through the system. Centrifugal pumps are an example of a frequently used pump in a plant as described herein. Turbines may be used for harnessing heat energy generated by the plant, such as to convert the heat energy into electrical energy and/or to power fans or other rotating equipment. Steam turbines, for example, are often used in a plant as described herein.

There are several types of compressors typically used in chemical and petrochemical plants and refineries, the most common of which are centrifugal compressors, axial compressors, and reciprocating compressors. Many compressors in a plant as described herein are arranged in parallel with a redundant backup compressor, which can be activated to prevent total shutdown when the original compressor needs to be taken offline. Centrifugal and axial compressors are dynamic compressors that operate by transferring energy from a set of rotating impeller blades to a gas, which is then converted into potential energy in the form of increased gas pressure by diffusers that slow the flow of the gas, creating a pressurized output gas. FIG. 2 illustrates an example of a centrifugal compressor 310, which includes an inlet or intake 311, a plurality of impellers 313 mounted on a rotatable shaft 318 and located downstream from the inlet 311, and a plurality of diffusers 316 each located following one of the impellers 313, and an outlet or discharge 317 at the far downstream end of the compressor 310. The compressor 310 operates by building pressure incrementally and sequentially in the diffusers 316. FIG. 3 illustrates an example of an axial compressor 320, which includes an inlet or intake 321 a plurality of impellers 323 mounted on a rotatable shaft 328 and located downstream from the inlet 321, a stator 324 that has a plurality of stator vanes 325 arranged in circumferential rings each located downstream from one of the impellers 323 and operates as a diffuser 326, and an outlet or discharge 327 at the far downstream end of the compressor 320.

Centrifugal or axial compressors may be referred to as dynamic compressors or turbomachinery. Such compressors often have other components immediately upstream and downstream that enhance or enable the functioning of the compressor. Examples of such equipment include isolation valves, a suction strainers, a compressor suction drum or separator, an anti-surge spillback takeoff, a feed mix node and combined feed exchanger for H₂ recycle, and an interstage drum or knockout drum.

Performance of one or more types of compressors may be affected by changes in gas conditions, including gas temperature and the composition and/or molecular weight of the gas, among other factors. Process control of capacity may be made by speed variation, suction throttling, or variable inlet guide vanes. Process control of reciprocating compressors may be by suction valve unloaders, fixed or variable head end clearance pockets, recycle of gas back to suction, and stepless capacity systems. Compressors can be put through a variety of extreme conditions, such as high temperatures and pressures and corrosive and aggressive components.

Surge is a common issue faced by all centrifugal and axial compressors. Surge occurs when the outlet or discharge pressure of the compressor is equal to or greater than the pressure generated by the impellers 313, 323. In a centrifugal or axial compressor, this phenomenon typically occurs within the final diffuser 316, 326 before the outlet 317, 327. When this occurs, the increased outlet pressure drives airflow temporarily backward toward the impeller or impellers 313, 323. Surge typically happens in an oscillatory manner and is often accompanied by rapid (even exponential) temperature increase. Various factors can cause surging, such as increased discharge pressure, improper valve cycling, change in gas composition (e.g., decreased molecular weight of the gas), ramping the feed rate too fast, improper limit stop set point on the valves, and other operational errors or malfunctions, among other factors. Surge can decrease the effectiveness and efficiency of the compressor, and the vibrations, thrust reversals, and temperature increases that result from surging can damage components of the compressor (sometimes quickly) and reduce the functional life of the compressor. For example, vibrations and thrust reversal can cause damage to bearings and seals, and potentially cause contact between rotating and stationary parts. As another example, temperature increases can cause damage to seals, thermal expansion of the rotor/impeller, and contact between rotating and stationary parts.

Each dynamic compressor has a surge limit that represents a limit on operation of the compressor. FIG. 4 illustrates the performance operating of a centrifugal compressor, plotting change in pressure (ΔP) against flow, with the surge limit forming a performance limitation of the compressor. Normal operation occurs between the lines defined by the maximum and minimum compressor speeds and to the right of the surge line, and surge occurs when the surge line is crossed. An axial compressor may perform in a similar manner.

Additional issues faced by centrifugal or axial compressors include bearing and seal failures, wear, fouling, and damage from contact between moving and non-moving components, among others. Such failures may be caused by vibrations, thrust reversals, excessive temperature, and unwanted chemicals in the feed gas. Some of these issues may directly or indirectly result from surging, but these issues may result from other causes as well, including other causes described elsewhere herein.

A reciprocating compressor is a positive-displacement compressor that operates by a moveable member, e.g., a piston and/or a membrane/diaphragm, moving to decrease the volume of a cylinder filled with a gas, thereby compressing the gas within the cylinder. FIG. 5A illustrates an example of a piston-type reciprocating compressor 330, which includes a cylinder 331 with an inlet or intake 332 and an outlet or discharge 333, with a piston 334 that reciprocates to draw gases in through the inlet 332 and compress gases contained in the cylinder 331. The inlet 332 and the outlet 333 may further have inlet and outlet valves to permit intake of gas through the inlet 332 and discharge of compressed gas through the outlet 333 at appropriate times. The opening and closing of such valves is critical to the operation of the compressor. The valves should operate smoothly and timely as well as fully open and close at appropriate times. Vibrations and contaminants can affect the performance and integrity of the valves and their operation The compressor 330 illustrated in FIG. 5B also includes a piston rod, a crosshead connected to the piston rod that rides within a crosshead guide, a crosshead pin, rider rings on the piston 334, a head end or cylinder end (CE) head, a pressure packing case, and a piston rod drop transducer. Many reciprocating compressors may include multiple cylinders 331 as shown in FIG. 5A, and the inlets in such an embodiment may be provided through an intake manifold or suction manifold. FIG. 5A also illustrates the valve heads 338 of the compressor 330. The piston(s) 334 in a reciprocating compressor 330 may be driven by a crankshaft 337 in one or more embodiments.

Reciprocating compressors often have other components immediately upstream and downstream that enhance or enable the functioning of the compressor. Examples of such equipment include an isolation valve, a suction strainer, an interstage cooler or aftercooler, and a discharge drum.

One issue facing reciprocating compressors is ingress of liquid contaminants, which may occur through a variety of mechanisms, such as improper separation between gas and liquid components at some point along the line, seal leakage, condensation caused by insufficient temperature at some point along the line and compounded by poor suction pipe layout. Liquids are incompressible, and therefore, ingress of liquids into the compressor can negatively affect operation of the compressor. Other contaminants, such as particulates or debris entrained in the gas flow, also present issues for reciprocating compressors. Ingress of liquid or other contaminants can damage a reciprocating compressor, and in particular may cause valve distress and failure. Ingress of such contaminants may also cause fouling of equipment, process drifting, and/or decreasing capacity and efficiency.

A turbine is a device that extracts energy from a fluid flow and converts it into work, e.g., mechanical or electrical power. FIG. 6 illustrates an example of a steam turbine 340 that may be used in a plant as described herein. The turbine 340 includes an inlet 341 that receives steam 342 from a steam source, a rotor 343 with blades 344 that are acted on by the impulse of the steam 342 flowing through the inlet 341 to turn the rotor 343, and a shaft 345 that is rotated by the rotor 343 to generate mechanical power (e.g., by connection to gears) or electrical power (e.g., by use of induction equipment). The steam then escapes through an outlet 346. Heat generated by various components of the plant may be harnessed to create steam at the steam source, which is passed to the steam turbine 340 for conversion to mechanical or electrical power.

Issues faced by turbines in petrochemical plants include failure of trip and throttle valves, damage to flow path components (e.g., stationary or rotating blades) due to “wet” steam that is not of sufficiently high temperature, and failure of the turbine for mechanical reasons.

Many compressors (e.g., reciprocating compressors) may be driven by fixed-speed motors.

In various embodiments described herein, as described in further detail below, different types of sensors may be used in and around rotating equipment components such as compressors and turbines, including centrifugal compressors, axial compressors, reciprocating compressors, and/or steam turbines as described above. Data from such sensors can then be analyzed in a manual and/or automated manner, and corrective actions or recommendations for such actions can be generated based on such analysis. It is understood that any sensor described herein may be configured for communicating the data gathered by the sensor to a computer system, including by various wired or wireless technologies. In one or more embodiments, each sensor described herein may include a wireless transmitter (or transceiver) for wirelessly communicating with a computer system. In another embodiment, some or all of the sensors described herein may include an individual processor and/or memory configured for processing communications to/from the computer system or processing and/or storing data independently or in conjunction with the computer system.

Sensor Data Collection and Processing

The system may include one or more computing devices or platforms for collecting, storing, processing, and analyzing data from one or more sensors. FIG. 11A depicts an illustrative computing system that may be implemented at one or more components, pieces of equipment (e.g., rotating equipment), and/or plants. FIG. 11A-FIG. 11E (hereinafter collectively “FIG. 11”), show, by way of illustration, various components of the illustrative computing system in which aspects of the disclosure may be practiced. It is to be understood that other components may be used, and structural and functional modifications may be made, in one or more other embodiments without departing from the scope of the present disclosure. Moreover, various connections between elements are discussed in the following description, and these connections are general and, unless specified otherwise, may be direct or indirect, wired or wireless, and/or combination thereof, and that the specification is not intended to be limiting in this respect.

FIG. 11A depicts an illustrative operating environment in which various aspects of the present disclosure may be implemented in accordance with example embodiments. The computing system environment 1000 illustrated in FIG. 11A is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality contained in the disclosure. The computing system environment 1000 may include various sensor, measurement, and data capture systems, a data collection platform 1002, a data analysis platform 1004, a control platform 1006, one or more networks, one or more remote devices 1054, 1056, and/or one or more other elements. The numerous elements of the computing system environment of FIG. 11A may be communicatively coupled through one or more networks. For example, the numerous platforms, devices, sensors, and/or components of the computing system environment may be communicatively coupled through a private network 1008. The sensors be positioned on various components in the plant and may communicate wirelessly or wired with one or more platforms illustrated in FIG. 11A. The private network 1008 may include, in some examples, a network firewall device to prevent unauthorized access to the data and devices on the private network 1008. Alternatively or additionally, the private network 1008 may be isolated from external access through physical means, such as a hard-wired network with no external, direct access point. The data communicated on the private network 1008 may be optionally encrypted for further security. Depending on the frequency of collection and transmission of sensor measurements and other data to the data collection platform 1002, the private network 1008 may experience large bandwidth usage and be technologically designed and arranged to accommodate for such technological issues. Moreover, the computing system environment 1000 may also include a public network 1010 that may be accessible to remote devices (e.g., remote device 1054, remote device 1056). In some examples, a remote device may be located not in the proximity (e.g., more than one mile away) of the various sensor, measurement, and data capture systems illustrated in FIG. 11A. In other examples, the remote device may be physically located inside a plant, but restricted from access to the private network 1008; in other words, the adjective “remote,” need not necessarily require the device to be located at a great distance from the sensor systems and other components.

Although the computing system environment of FIG. 11A illustrates logical block diagrams of numerous platforms and devices, the disclosure is not so limited. In particular, one or more of the logical boxes in FIG. 11 may be combined into a single logical box or the functionality performed by a single logical box may be divided across multiple existing or new logical boxes. For example, aspects of the functionality performed by the data collection platform 1002 may be incorporated into one or each of the sensor devices illustrated in FIG. 11A. As such, the data collection may occur local to the sensor device, and the enhanced sensor system may communicate directly with one or more of the control platform 1006 and/or data analysis platform 1004. An illustrative example of such an embodiment is contemplated by FIG. 11A. Moreover, in such an embodiment, the enhanced sensor system may measure values common to a sensor, but may also filter the measurements such just those values that are statistically relevant or of-interest to the computing system environment are transmitted by the enhanced sensor system. As a result, the enhanced sensor system may include a processor (or other circuitry that enables execution of computer instructions) and a memory to store those instructions and/or filtered data values. The processor may be embodied as an application-specific integrated circuit (ASIC), FPGA, or other hardware- or software-based module for execution of instructions. In another example, one or more sensors illustrated in FIG. 11A may be combined into an enhanced, multi-purpose sensor system. Such a combined sensor system may provide economies of scale with respect to hardware components such as processors, memories, communication interfaces, and others.

In yet another example, the data collection platform 1002 and data analysis platform 1004 may reside on a single server computer and depicted as a single, combined logical box on a system diagram. Moreover, a data store may be illustrated in FIG. 11A separate and apart from the data collection platform 1002 and data analysis platform 1004 to store a large amount of values collected from sensors and other components. The data store may be embodied in a database format and may be made accessible to the public network 1010; meanwhile, the control platform 1006, data collection platform 1002, and data analysis platform 1004 may be restricted to the private network 1008 and left inaccessible to the public network 1010. As such, the data collected from a plant may be shared with users (e.g., engineers, data scientists, others), a company's employees, and even third parties (e.g., subscribers to the company's data feed) without compromising potential security requirements related to operation of a plant. The data store may be accessible to one or more users and/or remote devices over the public network 1010.

Referring to FIG. 11A, process measurements from various sensor and monitoring devices may be used to monitor conditions in, around, and on process equipment (e.g., rotating equipment). Such sensors may include, but are not limited to, pressure sensors 1024, differential pressure sensors 1036, various flow sensors (including but not limited to orifice plate type 1013, disc sensors 1022, venturi 1038, other flow sensors 1030), temperature sensors 1012 including thermal cameras 1020 and skin thermocouples, capacitance sensors 1034, weight sensors 1032, gas chromatographs 1014, moisture sensors 1016, ultrasonic sensors 1018, position sensors, timing sensors, vibration sensors 1026, microphones 1028, level sensors 1046, liquid level (hydraulic fluid) sensors, and other sensors used in the refining and petrochemical industry. Further, process laboratory measurements may be taken using gas chromatographs 1014, liquid chromatographs, distillation measurements, octane measurements, and other laboratory measurements. System operational measurements also can be taken to correlate the system operation to the rotating equipment measurements.

In addition, sensors may include transmitters and/or deviation alarms. One or more sensors may be programmed to set off an alarm or alert. For example, if an actuator fails, sensor data may be used to automatically trigger an alarm or alert (e.g., an audible alarm or alert, a visual alarm or alert). Other sensors may transmit signals to a processor or a hub that collects the data and sends to a processor. For example, temperature and pressure measurements may be sent to a hub (e.g., data collection platform 1002). In one or more embodiments, temperature sensors 1012 may include thermocouples, fiber optic temperature measurement, thermal cameras 1020, and/or infrared cameras. Skin thermocouples may be applied to rotating equipment casing, or alternatively, to tubes, plates, or placed directly on a wall of a rotating equipment component. Alternatively, thermal (infrared) cameras 1020 may be used to detect hot spots in all aspects of the equipment. A shielded (insulated) tube skin thermocouple assembly may be used to obtain accurate measurements. One example of a thermocouple may be a removable Xtracto™ Pad. A thermocouple can be replaced without any additional welding. Clips and/or pads may be used for ease of replacement. Fiber Optic cable can be attached to the pipe, line, and/or vessel to provide a complete profile of temperatures.

Sensors may be also used throughout a plant or rotating equipment to detect and monitor various issues such as PV detection, surge detection, fouling, gas quality, dew point characteristics, and/or production levels. Sensors might be able to detect whether feed composition into the rotating equipment, such as pH, are outside of acceptable ranges leading to a corrosive environment or whether consumption of sacrificial anodes (in water services) is nearing completion and resulting in a corrosive environment. Sensors detecting outlet temperatures and pressure drops may be used to determine/predict flow and production rate changes.

Furthermore, flow sensors may be used in flow paths such as the inlet to the path, outlet from the path, or within the path. If multiple tubes are used, the flow sensors may be placed in corresponding positions in each of the rotating machines. In this manner, one can determine if one of the rotating machines is behaving abnormally compared to one or more others. Flow may be determined by pressure-drop across a known resistance, such as by using pressure taps. In other examples, flow may be inferred using fluid density in addition to suction and discharge pressures. Other types of flow sensors include, but are not limited to, ultrasonic, turbine meter, hot wire anemometer, vane meter, Kármán™, vortex sensor, membrane sensor (membrane has a thin film temperature sensor printed on the upstream side, and one on the downstream side), tracer, radiographic imaging (e.g. identify two-phase vs. single-phase region of channels), an orifice plate (e.g., which may in some examples, be placed in front of one or more tube or channels), pitot tube, thermal conductivity flow meter, anemometer, internal pressure flow profile.

Sensor data, process measurements, and/or calculations made using the sensor data or process measurements may be used to monitor and/or improve the performance of the equipment and parts making up the equipment, as discussed in further detail below. For example, sensor data may be used to detect that a desirable or an undesirable chemical reaction is taking place within a particular piece of equipment, and one or more actions may be taken to encourage or inhibit the chemical reaction. Chemical sensors may be used to detect the presence of one or more chemicals or components in the streams, such as corrosive species, oxygen, hydrogen, and/or water (moisture). Chemical sensors may use gas chromatographs, liquid chromatographs, distillation measurements, and/or octane measurements. In another example, equipment information, such as wear, efficiency, production, state, or other condition information, may be gathered and determined based on sensor data. Corrective action may be taken based on determining this equipment information. For example, if the equipment is showing signs of wear or failure, corrective actions may be taken, such as taking an inventory of parts to ensure replacement parts are available, ordering replacement parts, and/or calling in repair personnel to the site. Certain parts of equipment may be replaced immediately. Other parts may be safe to use, but a monitoring schedule may be adjusted. Alternatively or additionally, one or more inputs or controls relating to a process may be adjusted as part of the corrective action. These and other details about the equipment, sensors, processing of sensor data, and actions taken based on sensor data are described in further detail below.

Monitoring the rotating equipment and the processes using rotating equipment includes collecting data that can be correlated and used to predict behavior or problems in different rotating equipment used in the same plant or in other plants and/or processes. Data collected from the various sensors (e.g., measurements such as flow, pressure drop, thermal performance, vessel skin temperature at the top, expansion bellows leak, vibration, etc.) may be correlated with external data, such as environmental or weather data. Process changes or operating conditions may be able to be altered to preserve the equipment until the next scheduled maintenance period. Fluids may be monitored for corrosive contaminants and pH may monitored in order to predict higher than normal corrosion rates within the rotating equipment.

Systems Facilitating Sensor Data Collection

Sensor data may be collected by a data collection platform 1002. The sensors may interface with the data collection platform 1002 via wired or wireless transmissions. The data collection platform 1002 may continuously or periodically (e.g., every second, every minute, every hour, every day, once a week, once a month) transmit collected sensor data to a data analysis platform 1004, which may be nearby or remote from the data collection platform 1002.

Sensor data (e.g., temperature data) may be collected continuously or at periodic intervals (e.g., every second, every five seconds, every ten seconds, every minute, every five minutes, every ten minutes, every hour, every two hours, every five hours, every twelve hours, every day, every other day, every week, every other week, every month, every other month, every six months, every year, or another interval). Data may be collected at different locations at different intervals. For example, data at a known hot spot may be collected at a first interval, and data at a spot that is not a known hot spot may be collected at a second interval. The data collection platform transmit collected sensor data to a data analysis platform, which may be nearby or remote from the data collection platform.

The computing system environment of FIG. 11A includes logical block diagrams of numerous platforms and devices that are further elaborated upon in FIG. 11B, FIG. 11C, FIG. 11D, and FIG. 11E. FIG. 11B is an illustrative data collection platform 1002. FIG. 11C is an illustrative data analysis platform 1004. FIG. 11D is an illustrative control platform 1006. FIG. 11E is an illustrative remote device 1054. These platforms and devices of FIG. 11 include one or more processing units (e.g., processors) to implement the methods and functions of certain aspects of the present disclosure in accordance with the example embodiments. The processors may include general-purpose microprocessors and/or special-purpose processors designed for particular computing system environments or configurations. For example, the processors may execute computer-executable instructions in the form of software and/or firmware stored in the memory of the platform or device. Examples of computing systems, environments, and/or configurations that may be suitable for use with the disclosed embodiments include, but are not limited to, personal computers (PCs), server computers, hand-held or laptop devices, smart phones, multiprocessor systems, microprocessor-based systems, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

In addition, the platform and/or devices in FIG. 11 may include one or more memories include any of a variety of computer readable media. Computer-readable media may be any available media that may be accessed by the data collection platform 1002, may be non-transitory, and may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, object code, data structures, database records, program modules, or other data. Examples of computer-readable media may include random access memory (RAM), read only memory (ROM), electronically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by the data collection platform 1002. The memories in the platform and/or devices may further store modules that may include compiled software code that causes the platform, device, and/or overall system to operate in a technologically improved manner as disclosed herein. For example, the memories may store software used by a computing platform, such as operating system, application programs, and/or associated database.

Furthermore, the platform and/or devices in FIG. 11 may include one or more communication interfaces including, but are not limited to, a microphone, keypad, keyboard, touch screen, and/or stylus through which a user of a computer (e.g., a remote device) may provide input, and may also include a speaker for providing audio output and a video display device for providing textual, audiovisual and/or graphical output. Input may be received via one or more graphical user interfaces, which may be part of one or more dashboards (e.g., dashboard 1003, dashboard 1005, dashboard 1007). The communication interfaces may include a network controller for electronically communicating (e.g., wirelessly or wired) over a public network 1010 or private network 1008 with one or more other components on the network. The network controller may include electronic hardware for communicating over network protocols, including TCP/IP, UDP, Ethernet, and other protocols.

In some examples, one or more sensor devices in FIG. 11A may be enhanced by incorporating functionality that may otherwise be found in a data collection platform 1002. These enhanced sensor system may provide further filtering of the measurements and readings collected from their sensor devices. For example, with some of the enhanced sensor systems in the operating environment illustrated in FIG. 11A, an increased amount of processing may occur at the sensor so as to reduce the amount of data needing to be transferred over a private network 1008 in real-time to a computing platform. The enhanced sensor system may filter at the sensor itself the measured/collected/captured data and only particular, filtered data may be transmitted to the data collection platform 1002 for storage and/or analysis.

Referring to FIG. 11B, in one or more embodiments, a data collection platform 1002 may include one or more processors 1060, one or more memories 1062, and communication interfaces 1068. The memory 1062 may include a database 1064 for storing data records of various values collected from one or more sources. In addition, a data collection module 1066 may be stored in the memory 1062 and assist the processor 1060 in the data collection platform 1002 in communicating with, via the communications interface 1068, one or more sensor, measurement, and data capture systems, and processing the data received from these sources. In some embodiments, the data collection module 1066 may include computer-executable instructions that, when executed by the processor 1060, cause the data collection platform 1002 to perform one or more of the steps disclosed herein. In other embodiments, the data collection module 1066 may be a hybrid of software-based and/or hardware-based instructions to perform one or more of the steps disclosed herein. In some examples, the data collection module 1066 may assist an enhanced sensor system with further filtering the measurements and readings collected from the sensor devices. Although the elements of FIG. 11B are illustrated as logical block diagrams, the disclosure is not so limited. In particular, one or more of the logical boxes in FIG. 11B may be combined into a single logical box or the functionality performed by a single logical box may be divided across multiple existing or new logical boxes. Moreover, some logical boxes that are visually presented as being inside of another logical box may be moved such that they are partially or completely residing outside of that logical box. For example, while the database 1064 in FIG. 11B is illustrated as being stored inside one or more memories 1062 in the data collection platform 1002, FIG. 11B contemplates that the database 1064 may be stored in a standalone data store communicatively coupled to the data collection module 1066 and processor 1060 of the data collection platform 1002 via the communications interface 1068 of the data collection platform 1002.

In addition, the data collection module 1066 may assist the processor 1060 in the data collection platform 1002 in communicating with, via the communications interface 1068, and processing data received from other sources, such as data feeds from third-party servers and manual entry at the field site from a dashboard graphical user interface (e.g., via dashboard 1003). For example, a third-party server may provide contemporaneous weather data to the data collection module. Some elements of chemical and petrochemical/refinery plants may be exposed to the outside and thus may be exposed to various environmental stresses. Such stresses may be weather related such as temperature extremes (hot and cold), high wind conditions, and precipitation conditions such as snow, ice, and rain. Other environmental conditions may be pollution particulates such as dust and pollen, or salt if located near an ocean, for example. Such stresses can affect the performance and lifetime of equipment in the plants. Different locations may have different environmental stresses. For example, a refinery in Texas will have different stresses than a chemical plant in Montana. In another example, data manually entered from a dashboard graphical user interface (e.g., via dashboard 1003) (or other means) may be collected and saved into memory by the data collection module. Production rates may be entered and saved in memory. Tracking production rates may indicate issues with flows. For example, as fouling occurs, the production rate may fall if a specific outlet temperature can no longer be achieved at the targeted capacity and capacity has to be reduced to maintain the targeted outlet temperature.

Referring to FIG. 11C, in one or more embodiments, a data analysis platform 1004 may include one or more processors 1070, one or more memories 1072, and communication interfaces 1082. The memory 1072 may include a database 1074 for storing data records of various values collected from one or more sources. Alternatively or additionally, the database 1074 may be the same database as that depicted in FIG. 11B and the data analysis platform 1004 may communicatively couple with the database 1074 via the communication interface of the data analysis platform 1004. At least one advantage of sharing a database between the two platforms is the reduced memory requirements due to not duplicating the same or similar data. In addition, a data analysis module 1076 may be stored in the memory 1072 and assist the processor 1070 in the data analysis platform 1004 in processing and analyzing the data values stored in the database 1074. In some embodiments, the data analysis module 1076 may include computer-executable instructions that, when executed by the processor 1070, cause the data analysis platform 1004 to perform one or more of the steps disclosed herein. In other embodiments, the data analysis module 1076 may be a hybrid of software-based and/or hardware-based instructions to perform one or more of the steps disclosed herein. In some embodiments, the data analysis module 1076 may perform statistical analysis, predictive analytics, and/or machine learning on the data values in the database 1074 to generate predictions and models. For example, the data analysis platform 1004 may analyze sensor data to detect new problems and/or to monitor existing problems (e.g., to determine if an existing problem is growing, maintaining the same severity, or shrinking) in the equipment of a plant. The data analysis platform 1004 may compare temperature or other data from different dates to determine if changes are occurring. Such comparisons may be made on a monthly, weekly, daily, hourly, real-time, or some other basis.

Referring to FIG. 11C, the recommendation module 1078 in the data analysis platform 1004 may coordinate with the data analysis module 1076 to generate recommendations for adjusting one or more parameters for the operation of the plant environment depicted in FIG. 11A. In some embodiments, the recommendation module 1078 may communicate the recommendation to the command module 1080, which may generate command codes that may be transmitted, via the communications interface, to cause adjustments or halting/starting of one or more operations in the plant environment. The command codes may be transmitted to a control platform 1006 for processing and/or execution. In one or more embodiments, the command codes may be directly communicated, either wirelessly or in a wired fashion, to physical components at the plant such that the physical components include an interface to receive the commands and execute on them.

Although the elements of FIG. 11C are illustrated as logical block diagrams, the disclosure is not so limited. In particular, one or more of the logical boxes in FIG. 11C may be combined into a single logical box or the functionality performed by a single logical box may be divided across multiple existing or new logical boxes. Moreover, some logical boxes that are visually presented as being inside of another logical box may be moved such that they are partially or completely residing outside of that logical box. For example, while the database is visually depicted in FIG. 11C as being stored inside one or more memories in the data analysis platform 1004, FIG. 11C contemplates that the database may be stored in a standalone data store communicatively coupled to the data analysis module and processor of the data analysis platform 1004 via the communications interface of the data analysis platform 1004. Furthermore, the databases from multiple plant locations may be shared and holistically analyzed to identify one or more trends and/or patterns in the operation and behavior of the plant and/or plant equipment. In such a crowdsourcing-type example, a distributed database arrangement may be provided where a logical database may simply serve as an interface through which multiple, separate databases may be accessed. As such, a computer with predictive analytic capabilities may access the logical database to analyze, recommend, and/or predict the behavior of one or more aspects of plants and/or equipment. In another example, the data values from a database from each plant may be combined and/or collated into a single database where predictive analytic engines may perform calculations and prediction models.

Referring to FIG. 11D, in one or more embodiments, a control platform 1006 may include one or more processors 1084, one or more memories 1086, and communication interfaces 1092. The memory 1086 may include a database 1088 for storing data records of various values transmitted from a user interface, computing device, or other platform. The values may include parameter values for particular equipment at the plant. For example, some illustrative equipment at the plant that may be configured and/or controlled by the control platform 1006 include, but is not limited to, a feed switcher 1042, sprayer 1052, one or more valves 1044, one or more pumps 1040, one or more gates 1048, and/or one or more drains 1050. In addition, a control module 1090 may be stored in the memory and assist the processor in the control platform 1006 in receiving, storing, and transmitting the data values stored in the database. In some embodiments, the control module 1090 may include computer-executable instructions that, when executed by the processor 1084, cause the control platform 1006 to perform one or more of the steps disclosed herein. In other embodiments, the control module may be a hybrid of software-based and/or hardware-based instructions to perform one or more of the steps disclosed herein.

In a plant environment such as illustrated in FIG. 11A, if sensor data is outside of a safe range, this may be cause for immediate danger. As such, there may be a real-time component to the system such that the system processes and responds in a timely manner. Although in some embodiments, data could be collected and leisurely analyzed over a lengthy period of months, numerous embodiments contemplate a real-time or near real-time responsiveness in analyzing and generating alerts, such as those generated or received by the alert module in FIG. 11E.

Referring to FIG. 11E, in one or more embodiments, a remote device 1054 may include one or more processors 1093, one or more memories 1094, and communication interfaces 1099. The memory 1094 may include a database 1095 for storing data records of various values entered by a user or received through the communications interface. In addition, an alert module 1096, command module 1097, and/or dashboard module 1098 may be stored in the memory 1094 and assist the processor 1093 in the remote device 1054 in processing and analyzing the data values stored in the database. In some embodiments, the aforementioned modules may include computer-executable instructions that, when executed by the processor, cause the remote device 1054 to perform one or more of the steps disclosed herein. In other embodiments, the aforementioned modules may be a hybrid of software-based and/or hardware-based instructions to perform one or more of the steps disclosed herein. In some embodiments, the aforementioned modules may generate alerts based on values received through the communications interface. The values may indicate a dangerous condition or even merely a warning condition due to odd sensor readings. The command module 1097 in the remote device 1054 may generate a command that when transmitted through the communications interface to the platforms at the plant, causes adjusting of one or more parameter operations of the plant environment depicted in FIG. 11A. In some embodiments, the dashboard module 1098 may display a graphical user interface to a user of the remote device 1054 to enable the user to enter desired parameters and/or commands. These parameters/commands may be transmitted to the command module 1097 to generate the appropriate resulting command codes that may be then transmitted, via the communications interface, to cause adjustments or halting/starting of one or more operations in the plant environment. The command codes may be transmitted to a control platform 1006 for processing and/or execution. In one or more embodiments, the command codes may be directly communicated, either wirelessly or in a wired fashion, to physical components at the plant such that the physical components include an interface to receive the commands and execute them.

Although FIG. 11E is not so limited, in some embodiments the remote device 1054 may include a desktop computer, a smartphone, a wireless device, a tablet computer, a laptop computer, and/or the like. The remote device 1054 may be physically located locally or remotely, and may be connected by one of communications links to the public network 1010 that is linked via a communications link to the private network 1008. The network used to connect the remote device 1054 may be any suitable computer network including the Internet, an intranet, a wide-area network (WAN), a local-area network (LAN), a wireless network, a digital subscriber line (DSL) network, a frame relay network, an asynchronous transfer mode (ATM) network, a virtual private network 1008 (VPN), or any combination of any of the same. Communications links may be any communications links suitable for communicating between workstations and server, such as network links, dial-up links, wireless links, hard-wired links, as well as network types developed in the future, and the like. Various protocols such as transmission control protocol/Internet protocol (TCP/IP), Ethernet, file transfer protocol (FTP), hypertext transfer protocol (HTTP) and the like may be used, and the system can be operated in a client-server configuration to permit a user to retrieve web pages from a web-based server. Any of various conventional web browsers can be used to display and manipulate data on web pages.

Although the elements of FIG. 11E are illustrated as logical block diagrams, the disclosure is not so limited. In particular, one or more of the logical boxes in FIG. 11E may be combined into a single logical box or the functionality performed by a single logical box may be divided across multiple existing or new logical boxes. Moreover, some logical boxes that are visually presented as being inside of another logical box may be moved such that they are partially or completely residing outside of that logical box. For example, while the database is visually depicted in FIG. 11E as being stored inside one or more memories in the remote device 1054, FIG. 11E contemplates that the database may be stored in a standalone data store communicatively coupled, via the communications interface, to the modules stored at the remote device 1054 and processor of the remote device 1054.

Referring to FIG. 11, in some examples, the performance of operation in a plant may be improved by using a cloud computing infrastructure and associated methods, as described in US Patent Application Publication No. US2016/0260041, which was published Sep. 8, 2016, and which is herein incorporated by reference in its entirety. The methods may include, in some examples, obtaining plant operation information from the plant and/or generating a plant process model using the plant operation information. The method may include receiving plant operation information over the Internet, or other computer network (including those described herein) and automatically generating a plant process model using the plant operation information. These plant process models may be configured and used to monitor, predict, and/or optimize performance of individual process units, operating blocks and/or complete processing systems. Routine and frequent analysis of predicted versus actual performance may further allow early identification of operational discrepancies, which may be acted upon to optimize impact.

The aforementioned cloud computing infrastructure may use a data collection platform 1002 associated with a plant to capture data, e.g., sensor measurements, which may be automatically sent to the cloud infrastructure, which may be remotely located, where it may be reviewed to, for example, eliminate errors and biases, and used to calculate and report performance results. The data collection platform 1002 may include an optimization unit that acquires data from a customer site, other site, and/or plant (e.g., sensors and other data collectors at a plant) on a recurring basis. For cleansing, the data may be analyzed for completeness and corrected for gross errors by the optimization unit. The data may also be corrected for measurement issues (e.g., an accuracy problem for establishing a simulation steady state) and overall mass balance closure to generate a duplicate set of reconciled plant data. The corrected data may be used as an input to a simulation process, in which the process model is tuned to ensure that the simulation process matches the reconciled plant data. An output of the reconciled plant data may be used to generate predicted data using a collection of virtual process model objects as a unit of process design.

The performance of the plant and/or individual process units of the plant is/are compared to the performance predicted by one or more process models to identify any operating differences or gaps. Furthermore, the process models and collected data (e.g., plant operation information) may be used to run optimization routines that converge on an optimal plant operation for a given values of, e.g., feed, products, and/or prices. A routine may be understood to refer to a sequence of computer programs or instructions for performing a particular task.

The data analysis platform 1004 may include an analysis unit that determines operating status, based on at least one of a kinetic model, a parametric model, an analytical tool, and/or a related knowledge and/or best practice standard. The analysis unit may receive historical and/or current performance data from one or a plurality of plants to proactively predict one or more future actions to be performed. To predict various limits of a particular process and stay within the acceptable range of limits, the analysis unit may determine target operational parameters of a final product based on actual current and/or historical operational parameters. This evaluation by the analysis unit may be used to proactively predict future actions to be performed. In another example, the analysis unit may establish a boundary or threshold of an operating parameter of the plant based on at least one of an existing limit and an operation condition. In yet another example, the analysis unit may establish a relationship between at least two operational parameters related to a specific process for the operation of the plant. Finally in yet another example, one or more of the aforementioned examples may be performed with or without a combination of the other examples.

The plant process model predicts plant performance that is expected based upon the plant operation information. The plant process model results can be used to monitor the health of the plant and to determine whether any upset or poor measurement occurred. The plant process model is desirably generated by an iterative process that models at various plant constraints to determine the desired plant process model.

Using a web-based system for implementing the method of this disclosure may provide one or more benefits, such as improved plant performance due to an increased ability by plant operators to identify and capture opportunities, a sustained ability to bridge plant performance gaps, and/or an increased ability to leverage personnel expertise and improve training and development. Some of the methods disclosed herein allow for automated daily evaluation of process performance, thereby increasing the frequency of performance review with less time and effort required from plant operations staff.

Further, the analytics unit may be partially or fully automated. In one or more embodiments, the system is performed by a computer system, such as a third-party computer system, remote from the plant and/or the plant planning center. The system may receive signals and parameters via the communication network, and displays in real time related performance information on an interactive display device accessible to an operator or user. The web-based platform allows all users to work with the same information, thereby creating a collaborative environment for sharing best practices or for troubleshooting. The method further provides more accurate prediction and optimization results due to fully configured models. Routine automated evaluation of plant planning and operation models allows timely plant model tuning to reduce or eliminate gaps between plant models and the actual plant performance. Implementing the aforementioned methods using the web-based platform also allows for monitoring and updating multiple sites, thereby better enabling facility planners to propose realistic optimal targets.

FIGS. 12A-12B depict illustrative system flow diagrams in accordance with one or more embodiments described herein. As shown in FIG. 12A, in step 201, data collection platform 1002 may collect sensor data. In step 202, data collection platform 1002 may transmit sensor data to data analysis platform 1004. In step 203, data analysis platform 1004 may analyze data. In step 204, data analysis platform 1004 may send an alert to remote device 1054 and/or remote device 1056.

As shown in FIG. 12B, in step 205, data analysis platform 1004 may receive a command from remote device 1054 and/or remote device 1056. In some embodiments, the control platform 1006 may receive the command from remote device 1054 and/or remote device 1056. In step 206, data analysis platform 1004 may send a command to control platform 1006. In some embodiments, the command may be similar to the command received from remote device 1054 and/or remote device 1056. In some embodiments, data analysis platform 1004 may perform additional analysis based on the received command from remote device 1054 and/or remote device 1056 before sending a command to control platform 1006. In step 207, control platform 1006 may take corrective action. The corrective action may be based on the command received from data analysis platform 1004, remote device 1054, and/or remote device 1056. The corrective action may be related to one or more pieces of equipment (e.g., rotating equipment) associated with sensors that collected the sensor data in step 201.

FIG. 15 depicts an illustrative flow diagram in accordance with one or more embodiments described herein. The flow may be performed by one or more devices, which may be interconnected via one or more networks.

First, the one or more devices may collect 1402 sensor data. The sensor data may be from one or more sensors attached to one or more pieces of equipment (e.g., rotating equipment) in a plant. The sensor data may be locally collected and processed and/or may be locally collected and transmitted for processing.

After the sensor data is collected, the one or more devices may process 1404 the sensor data. The one or more devices may compare the data to past data from the one or more pieces of equipment, other pieces of equipment at a same plant, one or more pieces of equipment at a different plant, manufacturer recommendations or specifications, or the like.

After the sensor data is processed, the one or more devices may determine 1406 one or more recommendations based on the sensor data. The one or more recommendations may include recommendations of one or more actions to take based on the sensor data.

The one or more devices may send 1408 one or more alerts, which may include the determined recommendation. The one or more alerts may include information about the sensor data, about other data, or the like.

The one or more devices may receive 1410 a command to take an action (e.g., the recommended action, an action other than the recommended action, or no action). After receiving the command, the one or more devices may take 1412 the action. The action may, in some embodiments, include one or more corrective actions, which may cause one or more changes in the operation of the one or more pieces of equipment. The corrective action(s) may be taken automatically or after user confirmation, and/or the corrective action(s) may be taken without an accompanying alert being generated (and vice-versa).

FIG. 13 depicts an illustrative graphical user interface 1200 of an application that may be used for providing information received from one or more sensors or determined based on analyzing information received from one or more sensors, according to one or more embodiments described herein. The graphical user interface may be displayed as part of a smartphone application (e.g., running on a remote device, such as remote device 1054 or remote device 1056), a desktop application, a web application (e.g., that runs in a web browser), a web site, an application running on a plant computer, or the like.

The graphical user interface 1200 may include one or more visual representations of data (e.g., chart, graph, etc.) that shows information about a plant, a particular piece of equipment in a plant, or a process performed by a plant or a particular piece or combination of equipment in the plant. For example, a graph may show information about an operating condition, an efficiency, a production level, or the like. The graphical user interface 1200 may include a description of the equipment, the combination of equipment, or the plant to which the visual display of information pertains.

The graphical user interface 1200 may display the information for a particular time or period of time (e.g., the last five minutes, the last ten minutes, the last hour, the last two hours, the last 12 hours, the last 24 hours, etc.). The graphical user interface may be adjustable to show different ranges of time, automatically or based on user input.

The graphical user interface 1200 may include one or more buttons that allow a user to take one or more actions. For example, the graphical user interface may include a button (e.g., an “Actions” button) that, when pressed, shows one or more actions available to the user. The graphical user interface may include a button (e.g., a “Change View” button) that, when pressed, changes one or more views of one or more elements of the graphical user interface. The graphical user interface may include a button (e.g., a “Settings” button) that, when pressed, shows one or more settings of the application of which the graphical user interface is a part. The graphical user interface may include a button (e.g., a “Refresh Data” button) that, when pressed, refreshes data displayed by the graphical user interface. In some aspects, data displayed by the graphical user interface may be refreshed in real time, according to a preset schedule (e.g., every five seconds, every ten seconds, every minute, etc.), and/or in response to a refresh request received from a user. The graphical user interface may include a button (e.g., a “Send Data” button) that, when pressed, allows a user to send data to one or more other devices. For example, the user may be able to send data via email, SMS, text message, iMessage, FTP, cloud sharing, AirDrop, or via some other method. The user may be able to select one or more pieces of data, graphics, charts, graphs, elements of the display, or the like to share or send. The graphical user interface may include a button (e.g., an “Analyze Data” button) that, when pressed, causes one or more data analysis functions to be performed. In some aspects, the user may provide additional input about the desired data analysis, such as desired input, desired output, desired granularity, desired time to complete the data analysis, desired time of input data, or the like.

FIG. 14 depicts an illustrative graphical user interface 1300 of an application that may be used for providing alerts and/or receiving or generating commands for taking corrective action, in accordance with one or more embodiments described herein. The graphical user interface 1300 may include an alert with information about a current state of a piece of equipment (e.g., rotating equipment), a problem being experienced by a piece of equipment (e.g., rotating equipment), a problem with a plant, or the like.

The graphical user interface 1300 may include one or more buttons that, when pressed, cause one or more actions to be taken. For example, the graphical user interface 1300 may include a button that, when pressed, causes a flow rate to change. In another example, the graphical user interface 1300 may include a button that, when pressed, sends an alert to a contact (e.g., via a remote device), the alert including information similar to the information included in the alert provided via the graphical user interface. In a further example, the graphical user interface 1300 may include a button that, when pressed, shows one or more other actions that may be taken (e.g., additional corrective actions).

Reciprocating Compressor PV Detection and Part Replacement Process

In one or more embodiments, a reciprocating compressor system may be provided with sensors that assess the compression process by creating real-time P-V data (which may be displayed in the form of a diagram/graph) using a pressure sensor installed, e.g., on the head end and the crank end of each cylinder. In assessing the compression process and generating the P-V data, the computer system may require additional information, such as crank angle sensor data, compressor speed sensor data, and equipment geometry such as cylinder bore diameter, stroke length, piston rod diameter, and volumetric efficiency. The computer system can then receive the P and V data from the sensors and analyze the P and V data and suggest or commence corrective actions (if appropriate) based on that analysis. The corrective actions suggested/commenced may depend on the nature of the expected or predicted issue, and examples of such actions include generating a notification of a potential issue, predicting failure of parts (e.g., valves), arranging to have replacement parts shipped to the site, scheduling or otherwise assuring repair personnel are available at the site, replacing valves pre-failure or scheduling such replacement, and calculating a confidence level for the parts. Such actions may be done manually, automatically, or using a combination of manual and automated efforts, e.g., taking a manual action in response to an automated prompt.

A reciprocating compressor 330 as shown in FIG. 5A may include a pressure sensor 3 and one or more additional sensors 351 (e.g., a crank angle sensor and/or speed sensor) configured to monitor the P and V compression cycle within the cylinder 331 and/or at suction and discharge. The pressure sensor 350 and the additional sensor(s) 351 may be disposed within the inlet and/or outlet in one or more embodiments, measuring P and other parameter(s) based on suction pressure and volume and discharge pressure and volume. FIG. 7A illustrates these sensors 350, 351 positioned on the valve heads 338. One or both/all sensors 350, 351 may be disposed elsewhere in another embodiment, e.g., within the cylinder 331, and the location of the sensor may depend on the component/parameter to be monitored. The sensors 350, 351 are configured for continuous or substantially continuous detection, to permit collection of data dynamically during the operation of the compressor 330. For example, the P and V can be determined using the sensors 350, 351 for each stroke of the piston 334, and this data is transmitted to the computer system for recordation and/or further processing. This P and V data may include one or more of: maximum/minimum P, maximum/minimum V, and a continuous track of P and/or V throughout the stroke (e.g., multiple measurement points per second).

The compressor 330 may also be provided with vibration sensors 359 at one or more (or all) of the locations indicated in FIG. 7B to measure vibrations at various portions of the compressor 330. Vibration data can be indicative of problems with the compressor. The compressor 330 may also be provided with a temperature sensor 361 configured for monitoring the crosshead pin temperature, as shown in FIG. 7C. The temperature data from the crosshead pin can be an indirect indicator of rod reversal. This vibration data and temperature data can also be transmitted to the computer system for recordation and/or further processing. Further, this vibration and temperature data may be analyzed using the techniques described below with respect to the P and V data, and may be analyzed independently or together with the P and V data, depending on the potential characteristic to be investigated.

Sensor information may be gathered by one or more sensors and transmitted to data collection platform. Data collection platform may transmit the collected sensor data to data analysis platform, which may be at a plant or remote from a plant (e.g., in the cloud). Data analysis platform may analyze the received sensor data. Data analysis platform may compare the sensor data to one or more rules to determine if any of the issues disclosed herein are occurring. For example, predictions of a failure of parts (e.g., valves) may be indicated if in one or more conditions are met: (1) pressure sensor readings at the suction and discharge are outside an appropriate range, and/or (2) volume sensor readings at the suction and discharge are outside an appropriate range. Furthermore, data analysis platform may compare current sensor data to past sensor data from the rotating equipment, from other rotating equipment at the same plant, from other rotating equipment at other plants, from a manufacturer, or the like. Data analysis platform may determine if one or more data characteristics of the sensor data match data that may indicate any of the issues disclosed herein.

Data analysis platform may further run process simulations to suggest changes to operating parameters of the rotating equipment and associated components to avoid or limit further damage by one or more of the issues disclosed herein. In some aspects, data analysis platform may communicate with one or more vendors regarding the results of the simulation, and receive recommendations from the vendor on how to change or optimize parameters (e.g., geometry) of the equipment. Data analysis platform may use this information to create or expand a searchable database.

In one or more embodiments, the P and V data may be compared to current or archived P and V data for the same compressor and/or other compressors, and the computer system can analyze the data to make useful determinations, such as whether the data indicates that a potential issue exists or will exist and/or making predictions regarding future operation. Corrective actions can be taken if deviations are determined to exist, and if such deviations are determined to be potentially indicative of an issue. The data comparison may be made across a variety of time frames, from a single cycle, to a time frame of a few minutes or hours, to real-time continuous comparison, to historical comparison over a period of months or more, and may include absolute and proportional comparisons. As one example, a deviation may be detected if the P and/or V of the compressor is found to differ by a set percentage (e.g., +/−5% or 10%) from normal operation data. As another example, a deviation may be detected if the P and/or V of the compressor exceeds a specific absolute threshold, either as a set threshold or as a set absolute difference from normal operation data. The data analysis may be done over one or more different time frames, and the deviation percentage or threshold may depend on the time frame for comparison. In one or more embodiments, the difference from normal operation data required to detect a deviation over a short time frame may be relatively large as compared to analysis of a longer time frame, which may require a relatively smaller difference to detect a deviation. For example, a gradual but consistent increase or decrease in P/V over a long time frame may be used in predicting long-term failure.

The data comparison may also be made with respect to various different pieces of equipment. As one example, the data comparison may be limited to only the compressor in question. As another example, the data comparison may be made relative to other compressors in the system, and potentially to all other compressors of the same type within the system. As a further example, the data comparison may be made relative to historical data, including historical data for the same compressor or historical data for other compressors. It is understood that data analysis does not necessarily need to be done for the purpose of detecting deviations, as described in greater detail below. For example, data comparison that indicates consistency with historical data for a compressor that failed in a specific way may be valuable in predicting whether and when failure will occur.

The data used for the comparison may also depend on the stage of operation of the compressor. For example, start-up or shut-down of the machine may place increased stresses on the system and may require different data comparison. Different criteria (% or threshold) for deviation from normal operation may be applied during start-up or shut-down. Different comparison data may be used for analysis during start-up or shut-down as well, such as comparison to other start-up or shut-down data, rather than data from steady operation. As another example, different criteria and/or comparison data may be used during particular environmental conditions, such as based on a particular season or weather phenomenon.

In another example, the P and V data can be compared to previous trend or pattern P/V data from the same or other compressors. In this example, the overall trend or pattern of the P/V data for a compressor may be analyzed to determine which previous data sets match most closely to the present data. Once one or more similar trends in P/V data are matched, the matched data sets may provide useful predictive value. For example, matched data may be valuable in predicting whether and when failure will occur and/or which solutions may be effectively implemented to address an actual or potential issue.

Based on the analysis and comparison of data described herein, the computer system may take various actions, including corrective actions, notifications, predictions, etc. Corrective actions may include actions to correct a present issue or prophylactic actions to address predicted future issues. For example, the system can recommend and/or initiate alternative processes to preserve the life of the compressor. As another example, the system can predict a failure date for the compressor so that corrective actions can be taken, e.g., an alternate compressor can be prepared and then can be placed on-line.

The above analysis and/or actions may further incorporate additional data gathered by additional sensors in and around the compressor and/or elsewhere in the system in other embodiments. Examples of such additional data include the vibration of the compressor and the temperature of the crosshead pin as described above, as well as data from a piston rod drop detector, the spillback valve position, process conditions, stepless capacity regulation system loading percentage, the suction strainer ΔP, the composition of the fluid stream, the overall molecular weight of the fluid stream, and the temperature of the compressor or the inlet/outlet thereof. This additional data may influence determinations of potential problems and goals or may influence the corrective actions that are suggested and implemented.

Early Surge Detection and Correction

In one or more embodiments, one or more sensors may be used in conjunction with a centrifugal or axial compressor to detect a surge or potential surge in the equipment and allow corrective actions to be taken as quickly as possible. Surge can happen very rapidly, and correct measurement and analysis can detect a surge in the system at an early stage. If surge can be detected at an early stage or reliably predicted in advance, corrective actions can be taken to address the surge rather than risk an unscheduled shutdown of the process. An automated technique for detecting and managing potential surge permits the compressor to operate more closely to the surge limit line, increasing the potential operating envelope of the compressor. FIG. 8C illustrates typical surge behavior of a centrifugal or axial compressor.

A centrifugal compressor 310 as shown in FIG. 2 or an axial compressor 320 as shown in FIG. 3 may include a pressure sensor 352 and a temperature sensor 353 configured to measure the pressure (P) and the temperature (T) within the compressor 310, 320 and/or at suction/discharge. FIG. 8A illustrates a centrifugal compressor 310 outfitted with such pressure sensors (PT) 352 and temperature sensors (TT) 353, and it is understood that an axial compressor 320 may be outfitted in the same manner. The pressure sensor 352 and the temperature sensor 353 may be disposed within the inlet 311, 321 and/or outlet 317, 327 in one or more embodiments, measuring P and T based on suction pressure and temperature and discharge pressure and temperature. One or both sensors 352, 353 may be disposed elsewhere in another embodiment, e.g., within the chamber of the compressor 310, 320, at the impeller(s) 313, 323 and/or the diffuser(s) 316, 326. The sensors 352, 353 are configured for continuous or substantially continuous detection, to permit collection of data dynamically during the operation of the compressor 310, 320. This P and T data may include one or more of: maximum/minimum P, maximum/minimum T, and a continuous track of P and/or T throughout the operation (e.g., multiple measurement points per second). Additional sensors may be positioned and configured for measuring parameters such as gas flow speed, inlet guide vane angle, and/or suction throttle valve position. For example, a gas flow sensor (FT) 354 at the inlet 311 and a shaft speed sensor (ST) 358 on the shaft 318 are also illustrated in FIG. 8A. The flow sensor 354 may be a venturi sensor, an orifice plate, a compressor suction nozzle, an annubar, or a pitot tube, and an additional flow sensor 354 may be placed at the discharge side if desired. The data provided by these additional sensors 354, 358 may also be used by the computer system in conducting the analysis described below.

Sensors placed in and around the compressor can collect data relevant to potential surge occurrence and transmit the data to a computer system, which can analyze the data to determine or predict the approach of a potential surge event. The sensors can measure pressure and/or temperature at suction and at discharge in one or more embodiments, as described herein, and may additionally or alternately measure P and/or T within the chamber of the compressor. Data from other sensors measuring other parameters may also be included in the analysis by the computer system. The computer system can process the data from the sensors and evaluate the likelihood and/or timing of a potential surge condition. The system can process through a series of steps depending on the reaction of the system to each step.

Sensor information may be gathered by one or more sensors and transmitted to data collection platform. Data collection platform may transmit the collected sensor data to data analysis platform, which may be at a plant or remote from a plant (e.g., in the cloud). Data analysis platform may analyze the received sensor data. Data analysis platform may compare the sensor data to one or more rules to determine if any of the issues disclosed herein are occurring. For example, early detection of a surge may be indicated if one or more conditions are met: (1) pressure sensor readings at the suction and discharge are outside an appropriate range, and/or (2) temperature sensor readings at the suction and discharge are outside an appropriate range. Furthermore, data analysis platform may compare current sensor data to past sensor data from the rotating equipment, from other rotating equipment at the same plant, from other rotating equipment at other plants, from a manufacturer, or the like. Data analysis platform may determine if one or more data characteristics of the sensor data match data that may indicate any of the issues disclosed herein.

Data analysis platform may further run process simulations to suggest changes to operating parameters of the rotating equipment and associated components to avoid or limit further damage by one or more of the issues disclosed herein. In some aspects, data analysis platform may communicate with one or more vendors regarding the results of the simulation, and receive recommendations from the vendor on how to change or optimize parameters (e.g., geometry) of the equipment. Data analysis platform may use this information to create or expand a searchable database.

In one or more embodiments, the P and T (or other) data may be compared to current or archived data for the same compressor and/or other compressors, and the computer system can analyze the data to make useful determinations, such as whether the data indicates that a potential surge condition exists or will exist and/or making predictions regarding future surge conditions. Corrective actions can be taken if deviations are determined to exist, and if such deviations are determined to be potentially indicative of present or future surge condition. The data comparison may be made across a variety of time frames, from a time frame of a few minutes or hours, to real-time continuous comparison, to historical comparison over a period of months or more, and may include absolute and proportional comparisons. As one example, a deviation indicating a potential present or future surge condition may be detected if the P and/or T data is found to differ by a set percentage (e.g., +/−5% or 10%) from normal operation data. As another example, a deviation indicating a potential present or future surge condition may be detected if the P and/or T data exceeds a specific absolute threshold, either as a set threshold or as a set absolute difference from normal operation data. The data analysis may be done over one or more different time frames, and the percentage or threshold used for detection may depend on the time frame for comparison. In one or more embodiments, the difference from normal operation data required to detect a potential present or future surge condition over a short time frame may be relatively large as compared to analysis of a longer time frame, which may require a relatively smaller difference to detect a deviation. For example, a gradual but consistent increase or decrease in P/T over a long time frame may be used in predicting future surge conditions on a long-term time frame.

The data comparison may also be made with respect to various different pieces of equipment. As one example, the data comparison may be limited to only the compressor in question. As another example, the data comparison may be made relative to other compressors in the system, and potentially to all other compressors of the same type within the system. As a further example, the data comparison may be made relative to historical data, including historical data for the same compressor or historical data for other compressors. It is understood that data analysis does not necessarily need to be done for the purpose of detecting deviations from normal operation data, as described in greater detail below. For example, data comparison that indicates consistency with historical data for a compressor that failed in a specific way may be valuable in predicting whether and when surge will occur in the future.

The data used for the comparison may also depend on the stage of operation of the compressor. For example, start-up or shut-down of the machine may place increased stresses on the system and may require different data comparison. Different criteria (% or threshold) for deviation from normal operation may be applied during start-up or shut-down. Different comparison data may be used for analysis during start-up or shut-down as well, such as comparison to other start-up or shut-down data, rather than data from steady operation. As another example, different criteria and/or comparison data may be used during particular environmental conditions, such as based on a particular season or weather phenomenon.

In another example, the P and T (or other) data can be compared to previous trend or pattern data from the same or other compressors in order to detect a potential present or future surge condition. In this example, the overall trend or pattern of the P/T data for a compressor may be analyzed to determine which previous data sets match most closely to the present data. Once one or more similar trends in P/T data are matched, the matched data sets may provide useful predictive value. For example, matched data may be valuable in predicting whether and when a surge condition may or will occur and/or which solutions may be effectively implemented to address an actual or potential surge.

Based on the analysis and comparison of data described herein, the computer system may take various actions, including corrective actions, notifications, predictions, etc. Corrective actions may include actions to correct a present surge condition or prophylactic actions to address predicted future surge conditions. For example, the system can recommend and/or initiate alternative processes to preserve the life of the compressor. Examples of corrective actions that may be taken to address a potential present or future surge condition include taking measures to alter the processing conditions to make it incrementally more favorable for the compressor such as, but not limited to, changing the inlet gas density (such as changing gas temperature), speeding up the machine, adjusting system control valves within allowable limits to reduce load on the compressor, and/or otherwise reducing the system load to maximize overall process unit production given a compressor surge limit constraint or a combination of such techniques. Another potential corrective action is activating a re-cycle valve to reduce pressure near the discharge of the compressor, however, other corrective actions may be preferable if they can address the surge without requiring a re-cycle valve to be opened. Additionally, the system can use over-powered actuators to speed corrective actions when a near-surge event is detected. For example, such an actuator may be placed on the inlet butterfly valve in order to quickly address potential surge events. Taking such corrective actions to avoid a surge condition may permit the compressor to maintain normal operation until an appropriate time for repair, such as when the system reaches a turn-around event or scheduled shutdown, in order to avoid unscheduled shutdowns.

Further, corrective actions to avoid a potential surge may be implemented as successive or incremented steps, with each successive step depending on the effectiveness of a previous step or the severity/imminence of the potential surge event. The severity or cost of the corrective actions may increase as the severity/imminence of the potential surge event increases. FIG. 8B illustrates graphically how different actions can be taken as the operation of the compressor approaches nearer to the surge limit line (SLL). For example, a first corrective action (e.g., changing the inlet gas temperature) may be commenced when line C is reached, a second corrective action (e.g., opening the butterfly valve and/or speeding up the impellers) may be commenced when line B is reached, a third corrective action (e.g., reducing system load) may be commenced when line A is reached, and a failsafe corrective action (e.g., activating a re-cycle valve) may be commenced when the Surge Control line (SCL) is reached. In one or more embodiments, the position of the SLL may change based on various conditions such as wear on the compressor, and the computer system may predict the position of the SLL based on the data gathered by the sensors and adjust the positions of the control lines accordingly.

Reactor Loop Fouling Monitor

In one or more embodiments, one or more sensors may be used in conjunction with a centrifugal or axial compressor to detect potential fouling in the equipment and allow corrective actions to be taken. Fouling can occur in multiple locations on a dynamic compressor, and a frequent source of problems is fouling from buildup of nitrogen salts on the impeller blades, especially in a recycle gas compressor. If fouling can be detected at an early stage, corrective actions can be taken to address the fouling rather than shutting down the entire process. An automated technique for detecting and managing potential fouling can prevent unscheduled shutdowns of equipment.

A centrifugal compressor 310 as shown in FIG. 2 or an axial compressor 320 as shown in FIG. 3 may include one or more vibration sensors (A) 362 mounted on the compressor shaft 318, 328 and configured to measure vibrations in the shaft. The vibration sensor(s) 355 may be a proximity probe in one or more embodiments, and may be mounted adjacent to one of the bearings of the compressor 310, 320. In addition, a centrifugal or axial compressor 310, 320 as shown in FIGS. 2-3 may include one or more pressure sensors 356 and, optionally, one or more temperature sensors (T) configured to measure the pressure (P) and temperature (T), respectively, within the compressor 310 and/or at suction/discharge. In one or more embodiments, the compressor 310, 320 may include multiple pressure sensors 356 disposed after each of the impellers 313, 323, to permit measurement of the ΔP for each impeller 313, 323. Temperature sensors 355 may likewise be disposed adjacent each impeller 313, 323 to permit measurement of temperature for each impeller 313, 323. FIG. 9A illustrates this in connection with a centrifugal compressor 310, but it is understood that a similar configuration can be used with an axial compressor, with sensors 355, 356 following each impeller 323. Such sensors 355, 356 may be mounted in the stationary flow passage of the diffuser 316 following each impeller 313 of a centrifugal compressor in one or more embodiments. The sensors 355, 356 may be positioned at each diffuser 326 in an axial compressor 320. In another embodiment, the compressor 310 may additionally or alternately include pressure sensors 356 disposed within the inlet 311, 321 and outlet 317, 327, measuring P based on suction pressure and discharge pressure. Likewise, the temperature sensors 355 may be disposed at each end of the shaft 318, 328. One or both sensors 355, 356 may be disposed elsewhere in another embodiment. The sensors 355, 356, 362 are configured for continuous or substantially continuous detection, to permit collection of data dynamically during the operation of the compressor 310. This vibration and pressure data may include one or more of: maximum/minimum P, maximum/minimum vibration, and a continuous track of P and/or vibration throughout the operation (e.g., multiple measurement points per second). Increased vibrations and decreased output pressures are often indicative of fouling. Additional sensors may be positioned and configured for measuring parameters such as temperature. For example, temperature sensors could be located in similar positions as those described above for the pressure sensors 356. In other embodiments, the compressor 310 may not include pressure, temperature, and vibration sensors. For example, in one or more embodiments, the compressor 310 may include only pressure sensors 356 and temperature sensors 355, and in another embodiment, the compressor 310 may include only pressure sensors 356 and vibration sensors 362.

Sensors placed in and around the compressor can collect data relevant to potential fouling and transmit the data to a computer system, which can analyze the data to determine whether fouling exists, determine a degree of fouling, or predict a future occurrence and/or degree of fouling. In particular, the system may be configured to detect and/or predict fouling of the impeller blades for the compressor. In one or more embodiments, pressure and vibration data collected by the sensors 355, 356 may be used in this analysis. Additionally, the pressure and vibration data and analysis thereof may be based on the compressor as a whole or, in the case of a centrifugal compressor 310, may be based on each individual impeller 313. Analysis of data on a per-impeller basis can permit detection of whether fouling is limited to a particular impeller or impellers, so that corrective actions can be taken with respect to the particular impeller(s). The computer system may be configured to determine the extent of the vibration and pressure and/or the deviation of the vibration and pressure from standard operation.

Sensor information may be gathered by one or more sensors and transmitted to data collection platform. Data collection platform may transmit the collected sensor data to data analysis platform, which may be at a plant or remote from a plant (e.g., in the cloud). Data analysis platform may analyze the received sensor data. Data analysis platform may compare the sensor data to one or more rules to determine if any of the issues disclosed herein are occurring. For example, detecting of fouling of the impeller blades for a compressor may be indicated if in one or more conditions are met: (1) sensing of increased vibrations and/or (2) measuring of decreased output pressure. Furthermore, data analysis platform may compare current sensor data to past sensor data from the rotating equipment, from other rotating equipment at the same plant, from other rotating equipment at other plants, from a manufacturer, or the like. Data analysis platform may determine if one or more data characteristics of the sensor data match data that may indicate any of the issues disclosed herein.

Data analysis platform may further run process simulations to suggest changes to operating parameters of the rotating equipment and associated components to avoid or limit further damage by one or more of the issues disclosed herein. In some aspects, data analysis platform may communicate with one or more vendors regarding the results of the simulation, and receive recommendations from the vendor on how to change or optimize parameters (e.g., geometry) of the equipment. Data analysis platform may use this information to create or expand a searchable database.

In one or more embodiments, the P and vibration data may be compared to current or archived P and vibration data for the same compressor and/or other compressors, and the computer system can analyze the data to make useful determinations, such as whether the data indicates that potential fouling exists or will exist and/or making predictions regarding future operation.

Corrective actions can be taken if deviations are determined to exist, and if such deviations are determined to be potentially indicative of fouling. The data comparison may be made across a variety of time frames, from a time frame of a few minutes or hours, to real-time continuous comparison, to historical comparison over a period of months or more, and may include absolute and proportional comparisons. As one example, fouling may be detected if the P and/or vibration of the compressor (or an individual impeller) is found to differ by a set percentage (e.g., +/−5% or 10%) from normal operation data. As another example, a deviation may be detected if the P and/or vibration of the compressor (or an individual impeller) exceeds a specific absolute threshold, either as a set threshold or as a set absolute difference from normal operation data. The data analysis may be done over one or more different time frames, and the deviation percentage or threshold may depend on the time frame for comparison. In one or more embodiments, the difference from normal operation data required to detect a deviation over a short time frame may be relatively large as compared to analysis of a longer time frame, which may require a relatively smaller difference to detect a deviation. For example, a gradual but consistent increase or decrease in pressure or vibration over a long time frame may be used in predicting long-term failure. For detecting fouling of impeller blades in particular, vibration data often follows a predictable increase over time when monitored continuously. But such vibration data may, in some embodiments, be more effective in predicting fouling when considered in conjunction with other data (e.g., pressure data).

The data comparison may also be made with respect to various different pieces of equipment. As one example, the data comparison may be limited to only the compressor (or impeller) in question. As another example, the data comparison may be made relative to other compressors (or impellers) in the system, and potentially to all other compressors (or impellers) of the same type within the system. As a further example, the data comparison may be made relative to historical data, including historical data for the same compressor (or impeller) or historical data for other compressors (or impellers). It is understood that data analysis does not necessarily need to be done for the purpose of detecting deviations, as described in greater detail below. For example, data comparison that indicates consistency with historical data for a compressor (or impeller) that exhibited fouling may be valuable in predicting whether and when a specific degree of fouling will occur.

The data used for the comparison may also depend on the stage of operation of the compressor. For example, start-up or shut-down of the machine may place increased stresses on the system and may require different data comparison. Different criteria (% or threshold) for deviation from normal operation may be applied during start-up or shut-down. Different comparison data may be used for analysis during start-up or shut-down as well, such as comparison to other start-up or shut-down data, rather than data from steady operation. As another example, different criteria and/or comparison data may be used during particular environmental conditions, such as based on a particular season or weather phenomenon.

In another example, the P and vibration data can be compared to previous trend or pattern P or vibration data from the same or other compressors (or impellers). In this example, the overall trend or pattern of the P and vibration data for a compressor (or impeller) may be analyzed to determine which previous data sets match most closely to the present data. Once one or more similar trends in P or vibration data are matched, the matched data sets may provide useful predictive value. For example, matched data may be valuable in predicting whether and when a specific degree of fouling will occur and/or which solutions may be effectively implemented to address an actual or potential fouling issue. In particular, corrective actions that were effective for treating issues for a past compressor or impeller with matching data may be used with some expectation of effectiveness.

Based on the analysis and comparison of data described herein, the computer system may take various actions, including corrective actions, notifications, predictions, etc. Corrective actions may include actions to correct a present fouling condition or prophylactic actions to address predicted future fouling conditions. The corrective actions taken may depend on a degree of fouling detected or predicted. For example, the system can recommend and/or initiate alternative processes to preserve the life of the compressor. Such alternative processes that can be implemented include running an online wash, adjusting processing parameters, feed type/quality, and/or upstream or downstream unit operations, slowing the impeller speed, adding (or putting online) a guard bed that pulls nitrogen out of the feed gas, using a feed gas with less nitrogen, or a combination of such actions, or other actions. In a centrifugal compressor, some alternative processes or other corrective actions may be taken on an individual impeller or impellers where possible. For example, as shown in FIG. 9B, a centrifugal compressor 310 may be provided with individual injection ports 357 in front of each impeller 313 to permit a wash to be injected in advance of a desired impeller 313. An axial compressor 320 may be configured in a similar manner. The effect of a wash may decrease for each successive impeller 313 that the wash passes through, because the heat of compression can vaporize a portion of the wash fluid. Thus, the configuration of the ports 357 in FIG. 9B enables the wash to be injected at a downstream impeller or impellers 313, bypassing upstream impellers 313 that exhibit no fouling issues. For a naphtha wash, this permits a smaller amount of naphtha wash to be used, which is advantageous in improving conversion of the system. For a reformate wash, this can reduce reprocessing costs. This data collection also allows multiple parties within the supply chain to share data and more quickly recognize underperformance or troubleshoot reported field problems, thereby adjusting process operations earlier or discovering the causes of problems and/or solutions more quickly. Further, the system can predict a failure date for a compressor (or an impeller) so that corrective actions can be taken. Taking corrective actions as described herein to address a detected or predicted fouling condition may permit the compressor to maintain normal operation until an appropriate time for repair, such as when the system reaches a turn-around event or scheduled shutdown, in order to avoid unscheduled shutdowns.

The above analysis and/or actions may further incorporate additional data gathered by additional sensors in and around the compressor and/or elsewhere in the system in other embodiments. This additional data may influence determinations of potential problems and goals or may influence the corrective actions that are suggested and implemented. For example, temperature sensors may be used in addition to or instead of vibration sensors for detecting and predicting fouling in one or more embodiments. As another example, the ΔP data from a suction strainer or a CFE may be used to detect or predict fouling in those components.

Additionally, the principles for detection and prediction of fouling described herein may be applied to detection and prediction of fouling for other equipment within the plant or components of such equipment. Examples of other equipment that may be suitable for use with these principles include (without limitation) an FCC main air blower, an FCC wet gas compressor, an Oleflex main reactor effluent compressor, a hydrotreating recycle machine, compressors in MTO and olefin recovery applications, and a crude saturated gas compressor.

Sensor Locations

As described herein, multiple different types of sensors may be used in connection with multiple different types of equipment and/or components of such equipment, to collect data regarding the operation of such equipment and/or components thereof. These sensors can be combined with memories, processors, and transmitters (or transceivers) as described herein. In one or more embodiments, a sensor provided with a processor can process the data directly and instruct actions based on the data processing. This enables the sensor/processor to analyze the data quickly and in real time and to quickly instruct further actions to be taken based on such analysis. In another embodiment, a sensor provided with a transmitter can transmit the data to the computer system and/or other processors for data processing. Communication among the sensors can permit greater integration of data for a more complete view of potential problems and corrective actions to address such problems. Examples of sensors that can be used include thermocouples, temperature-sensing IR guns, thermography cameras, pressure sensors, vibration sensors, position sensors, and other sensors described herein. Such sensors may be placed in strategic locations in specific articles of equipment in order to monitor the equipment or desired components thereof in specific ways.

In one or more embodiments, sensors may be placed in a reciprocating compressor, such as the reciprocating compressor 330 illustrated in FIG. 5A. For example, a temperature sensor configured for monitoring the relief valve, the drain valve, or the vent valve may be used to determine whether the valve is leaking (e.g., not seating correctly), which may reduce compressor performance. A position sensor may additionally or alternately be used to monitor whether these or other valves are operating properly. Increases in temperature downstream of these valves may indicate leakage. FIG. 10A illustrates a reciprocating compressor operating within the system, and FIG. 10B illustrates the locations of a relief valve, a drain valve, and a vent valve in the system, and locations where temperature sensors may be added according to this embodiment. As another example, temperature sensors may be configured for monitoring compressor valve covers in a reciprocating compressor. Locations of increased temperature are signs of distress in valve covers, and such data can enable identification of problematic valves on a multi-valve compressor. Examples of temperature sensors that may be used for this purpose include one or more skin thermocouples or an automated thermal camera with relatively high thermal resolution.

In another embodiment, a position sensor 363 may be placed on a pipe support spring hanger 364 (which may have a travel indicator), as shown in FIG. 10C. The pipe support spring hanger 364 is configured for supporting a pipe within the system, such as a suction or discharge pipe for a compressor, pump, or turbine as described herein. Excessive forces and momenta applied by system suction and/or discharge piping can cause distortion of the casing for a compressor, pump, turbine, or other equipment and can contribute directly to key sub-component (e.g., seal or bearing) reliability. Fans are a particularly susceptible piece of equipment to damage from excessive pipe forces and moments. The data gathered by the position sensor 363 can be analyzed to determine positional deviations that may indicate excessive forces and/or moments on the pipe(s) that are being supported by the spring hanger 364. If the position data indicates a present or predicted future failure in the pipe or a piece of equipment to which the pipe is connected, the computer system may take corrective action, including recommending and/or scheduling work for repair or reinforcement. It is understood that various types of spring-based pipe supports may be used in connection with systems as described herein, including hanger non-hanging supports, and that a position sensor 363 may be advantageously used with any spring-based pipe support.

In another embodiment, sensors may be placed in a centrifugal compressor, such as the centrifugal compressor 310 illustrated in FIG. 2. For example, the compressor 310 may include one or more sensors disposed after each of the impellers 313, to permit measurement of data for each impeller 313. Pressure sensors, flow sensors, temperature sensors, or other sensors may be positioned and used in this manner. Such sensors may be mounted in the stationary flow passage of the diffuser 316 following each impeller 313 in one or more embodiments. FIG. 10H illustrates the location of pressure sensors (P) 356 and temperature sensors (T) 355 for each impeller stage 313 of the centrifugal compressor 310 (or an axial compressor), and it is understood that other sensors could be disposed on an individual impeller basis as well. Additional examples of sensors that may be added to centrifugal compressors and associated equipment include:

-   -   Sensor for sensing the amount of flow through the balance piston         is determined. If it is determined that the amount of flow has         deviated more than a preset amount from a standard state, an         alarm is triggered. The amount of deviation that triggers and         alarm can be higher for short term events (e.g., 10% in 5 min)         and lower for long term events (e.g., gradual increase of a         total of 3% over period of 6 mos.). The flow/pressure signature         for a particular balance piston can also be constantly compared         to flow/pressure signatures from past operations of the same         compressor and past operations of other compressors. When the         flow/pressure signature matches or is a near-match for a         flow/pressure signature that indicates potential problems,         corrective action is taken.     -   A sensor for measuring power directly, such as a torque meter on         the shaft of a centrifugal compressor, to directly measure power         of the motor from electrical input. This provides a direct         indication of operating expense and allows determination of         efficiency changes in the equipment. An amp meter could         alternately be used to provide an indirect indication of power.         The torque meter could also be used on the shaft of any other         rotating equipment, including a reciprocating compressor, an         axial compressor, a centrifugal pump, a steam turbine, etc.     -   Pressure sensor for measuring ΔP across a strainer for a         centrifugal compressor or any other rotating equipment. A single         differential pressure instrument with sensors at the inlet and         outlet of the strainer may be used for this purpose. This can be         used in determining blockage of the strainer, including blockage         due to fouling, as well as prediction of future degrees of         fouling. Such blockage can cause high temperature in a         reciprocating compressor, surge in a centrifugal compressor, and         other operational issues and inefficiencies.

In another embodiment, a pressure sensor (P) and/or a temperature sensor (T) may be placed on the seal gland of a centrifugal pump 360, as shown in FIG. 10D, to determine whether a throat bushing is wearing out. This permits corrective action, such as switching to a standby pump and refurbishing the throat bushing instead of suffering mechanical shaft seal failure. For reference, FIG. 10E illustrates example usage of centrifugal pumps within the system.

In another embodiment, one or more pressure sensors (P) may be configured and located to infer flow across the balance piston of a centrifugal pump 360, by detecting pressure on the working surface of the balance piston (e.g., pressure in the balance line), as shown in FIG. 10F. This can be used to determine fluid leakage of the balance piston. The centrifugal pump 360 uses a thrust bearing and a balance piston to compensate for the net hydraulic thrust force generated by the pump and to maintain a safe distance axially between the impeller(s) and stationary parts. The balance piston counteracts this force by using a large surface area exposed to pump suction pressure. The balance line is a piece of pipe that equalizes the pressure on the low pressure side of the balance piston with pump suction pressure. The condition of the balance piston can deteriorate over time, which reduces the overall pump capacity as there is more internal recirculation, and may compromise the mechanical integrity of the pump, as the balance piston back pressure rises and reduces its contribution to offsetting overall thrust. Multi-stage machines are much more complex, costly and time consuming to repair. Determining present or predicted future leakage permits corrective action, such as switching to a standby pump and addressing the fluid leakage instead of suffering failure.

In another embodiment, one or more temperature sensors may be placed on the heat exchanger for the seal flush cooler for a centrifugal pump, at the outlet connection and optionally also at the inlet connection, as shown in FIG. 10G. This temperature data can be analyzed to determine the existence or predict the future occurrence of fouling of the seal flush cooler, the cooling water is interrupted or seal flush line plugs. Determining present or predicted future issues of this type permits corrective action, such as switching to a standby pump and addressing issue instead of suffering failure of a mechanical seal.

In another embodiment, pressure and temperature sensors may be placed on a steam line for a steam turbine. Steam provided to a steam turbine may be superheated to avoid moisture buildup. Detection of the pressure and temperature of the steam line can determine qualitatively whether wet steam entered the turbine and/or quantitatively how much wet steam entered the turbine. Analysis of this data can also be used in evaluating the work developed by the turbine (and potential improvements therein) and to check the quality of the steam to determine potential upstream problems.

In another embodiment, thermocouples or other local temperature sensors are placed on an insulated pipe in the system underneath the insulation. This can be used in connection with the steam line for a steam turbine as described above, for steam heat tracing (e.g., on cylinders for a reciprocating compressor), or with other pipes within the plant. The temperature readings can be used to determine if the insulation is not sufficiently effective, such as if the insulation has been broken, damaged, or inadvertently not replaced after removal, or if rainwater or other contaminant has infiltrated the insulation and caused temperature issues. Alternately, conductive insulation can be used, an electric current is passed through the insulation, and a sensor can measure the current passing through the insulation. The current readings can be used to determine if the insulation has been broken or damaged. If issues are detected with regard to the insulation, the computer system can initiate corrective actions.

In another embodiment, a current sensor can be connected to electrical heat tracing on a cylinder for a reciprocating compressor or other component to determine whether the electrical heat tracing is working correctly, e.g., it has been deactivated or malfunctioned. If issues are detected with regard to the electrical heat tracing, the computer system can initiate corrective actions.

In another embodiment, a level sensor may be applied to any drains or drainage equipment to determine whether and how much condensation is accumulating and serve as an additional layer of equipment protection during starting. Condensation in a petrochemical system is often an indication of a problem along the line, and the level sensor data may be analyzed to determine a potential source of the problem. The computer system may generate alerts based on the potential source of the problem and/or the general presence of unexpected condensation at the location. The computer system may also initiate corrective action, such as opening the drain to permit condensation to be removed from the system.

The data collected by the sensors in the various locations and equipment described above may be processed and analyzed by the computer system in order to detect present issues, predict future issues, and/or initiate alerts or corrective actions related to such issues. Sensor information may be gathered by one or more sensors and transmitted to data collection platform. Data collection platform may transmit the collected sensor data to data analysis platform, which may be at a plant or remote from a plant (e.g., in the cloud). Data analysis platform may analyze the received sensor data. Data analysis platform may compare the sensor data to one or more rules to determine if any of the issues disclosed herein are occurring. For example, a leaky valve may be indicated if one or more conditions are met: (1) increase in temperature downstream of the valve, and/or (2) increase temperature readings by a skin thermocouple at a valve cover. Other potential failures and/or conditions may be indicated based on other data, as described above. Furthermore, data analysis platform may compare current sensor data to past sensor data from the rotating equipment, from other rotating equipment at the same plant, from other rotating equipment at other plants, from a manufacturer, or the like. Data analysis platform may determine if one or more data characteristics of the sensor data match data that may indicate any of the issues disclosed herein.

Data analysis platform may further run process simulations to suggest changes to operating parameters of the rotating equipment and associated components to avoid or limit further damage by one or more of the issues disclosed herein. In some aspects, data analysis platform may communicate with one or more vendors regarding the results of the simulation, and receive recommendations from the vendor on how to change or optimize parameters (e.g., geometry) of the equipment. Data analysis platform may use this information to create or expand a searchable database.

In one or more embodiments, the collected data may be compared to current or archived data for the same equipment or component, and the computer system can analyze the data to make useful determinations, such as whether the data indicates that a potential issue exists or will exist and/or making predictions regarding future operation. Corrective actions can be taken if deviations are determined to exist, and if such deviations are determined to be potentially indicative of an issue. The data comparison may be made across a variety of time frames, from a single cycle, to a time frame of a few minutes or hours, to real-time continuous comparison, to historical comparison over a period of months or more, and may include absolute and proportional comparisons. As one example, a deviation may be detected if a particular parameter is found to differ by a set percentage (e.g., +/−5% or 10%) from normal operation data. As another example, a deviation may be detected if a particular parameter exceeds a specific absolute threshold, either as a set threshold or as a set absolute difference from normal operation data. The data analysis may be done over one or more different time frames, and the deviation percentage or threshold may depend on the time frame for comparison. In one or more embodiments, the difference from normal operation data required to detect a deviation over a short time frame may be relatively large as compared to analysis of a longer time frame, which may require a relatively smaller difference to detect a deviation. For example, a gradual but consistent increase or decrease in a particular measurement over a long time frame may be used in predicting long-term failure.

The data comparison may also be made with respect to various different pieces of equipment. As one example, the data comparison may be limited to only the equipment or component in question. As another example, the data comparison may be made relative to other equipment or components in the system, and potentially to all other equipment and/or components of the same type within the system. As a further example, the data comparison may be made relative to historical data, including historical data for the same equipment or component, or historical data for other equipment or components. It is understood that data analysis does not necessarily need to be done for the purpose of detecting deviations, as described in greater detail below. For example, data comparison that indicates consistency with historical data for a piece of equipment or a component that failed in a specific way may be valuable in predicting whether and when failure will occur.

The data used for the comparison may also depend on the stage of operation of the equipment or component. For example, start-up or shut-down of the machine may place increased stresses on the system and may require different data comparison. Different criteria (% or threshold) for deviation from normal operation may be applied during start-up or shut-down. Different comparison data may be used for analysis during start-up or shut-down as well, such as comparison to other start-up or shut-down data, rather than data from steady operation. As another example, different criteria and/or comparison data may be used during particular environmental conditions, such as based on a particular season or weather phenomenon.

In another example, the data can be compared to previous trend or pattern data from the same or other equipment or components. In this example, the overall trend or pattern of the data for the equipment or component may be analyzed to determine which previous data sets match most closely to the present data. Once one or more similar trends in data are matched, the matched data sets may provide useful predictive value. For example, matched data may be valuable in predicting whether and when failure will occur and/or which solutions may be effectively implemented to address an actual or potential issue.

Based on the analysis and comparison of data described herein, the computer system may take various actions, including corrective actions, notifications, predictions, etc. Corrective actions may include actions to correct a present issue or prophylactic actions to address predicted future issues. For example, the system can recommend and/or initiate alternative processes to preserve the life of the equipment or component. Examples of alternative processes that can be implemented are given above and will depend on the equipment or component and the potential issue detected or predicted. As another example, the system can predict a failure date for the equipment or component so that corrective actions can be taken, e.g., an alternate article of equipment or component can be prepared and then can be placed on-line.

The above analysis and/or actions may further incorporate additional data gathered by additional sensors in and around the equipment or component and/or elsewhere in the system in other embodiments. Combinations of the data types discussed above may be used in analysis by the computer system. This additional data may influence determinations of potential problems and goals or may influence the corrective actions that are suggested and implemented.

Gas Quality for Dynamic Compressors

In one or more embodiments, sensors at various locations within the system can be used to measure various properties of the gas within the system, and this data is analyzed to determine the quality of the gas in the system. The computer system may take actions, such as generating alerts or initiating corrective action, based on the determined gas quality. The dry gas seals of a centrifugal compressor are one component at particular risk of failure from liquid condensation due to poor gas quality, and altering the gas flow may be necessary to avoid failure of the dry gas seals.

Several different combinations of sensors and techniques may be used to determine the composition of the gas in the system. In one or more embodiments, a gas chromatograph may be used to directly measure composition of the gas, although this may not permit continuous and real-time measurement. In another example, the density of the gas may be measured using a Coriolis mass flow meter, and the composition of the gas may be inferred from the density. In another example, a thermal mass meter may be used to measure heat capacity of the gas, and the composition of the gas may be inferred from the heat capacity. In another example, the temperature at the separator may be measured, and the amount of heavy gases (e.g., benzene) in the process gas may be inferred from the temperature in the separator. Other sensors, such as flow, pressure, and temperature sensors may aid in the determination or inferring of gas composition. The computer system receives data gathered according to one or more of these sensor configurations and analyzes the data to determine the composition of the gas using these methods.

Once the composition of the gas is determined, the computer system can determine whether the composition is indicative of the present or future occurrence of an issue such as condensation or component (e.g., seal) failure, and take actions if appropriate based on the determination. The quantity of heavy components in the gas flow can be determined using analysis of the data described above, and this quantity can be compared to other operational data during analysis by the computer system as described below. Alternately, the computer system may base the analysis on the raw data, e.g., density, heat capacity, separator temperature, etc., rather than the inferred composition. The raw data may be more conducive to quantitative analysis in some circumstances. The analysis performed by the computer system may include modeling of the conditions in the treating system, including temperature and pressure, to predict whether condensation will occur based on the determined or inferred composition of the gas or the data from which the composition is inferred. The modeling may also be used to predict the dew point of the gas and/or the amount of condensation of the gas, which can be used in quantitative analysis to determine a deviation from normal operating conditions or other indication of present or future condensation.

Sensor information may be gathered by one or more sensors and transmitted to data collection platform. Data collection platform may transmit the collected sensor data to data analysis platform, which may be at a plant or remote from a plant (e.g., in the cloud). Data analysis platform may analyze the received sensor data. Data analysis platform may compare the sensor data to one or more rules to determine if any of the issues disclosed herein are occurring. For example, poor gas quality may be indicated if one or more conditions are met: (1) inappropriate heat capacity of the gas as measured by a thermal mass meter, and/or (2) an inappropriate amount of heavy gas as inferred from the temperature measured in the separator. Furthermore, data analysis platform may compare current sensor data to past sensor data from the rotating equipment, from other rotating equipment at the same plant, from other rotating equipment at other plants, from a manufacturer, or the like. Data analysis platform may determine if one or more data characteristics of the sensor data match data that may indicate any of the issues disclosed herein.

Data analysis platform may further run process simulations to suggest changes to operating parameters of the rotating equipment and associated components to avoid or limit further damage by one or more of the issues disclosed herein. In some aspects, data analysis platform may communicate with one or more vendors regarding the results of the simulation, and receive recommendations from the vendor on how to change or optimize parameters (e.g., geometry) of the equipment. Data analysis platform may use this information to create or expand a searchable database.

In one or more embodiments, the collected data may be compared to current or archived data, and the computer system can analyze the data to make useful determinations, such as whether the data indicates that a potential gas quality issue exists or will exist and/or making predictions regarding future operation. The data comparison may be made across a variety of time frames, from a single point measurement, to a time frame of a few minutes or hours, to real-time continuous comparison, to historical comparison over a period of months or more, and may include absolute and proportional comparisons. As one example, a deviation from normal data may be detected if a component of the data is found to differ by a set percentage (e.g., +/−5% or 10%) from normal operation data. As another example, a deviation may be detected if the data exceeds a specific absolute threshold, either as a set threshold or as a set absolute difference from normal operation data. The data analysis may be done over one or more different time frames, and the deviation percentage or threshold may depend on the time frame for comparison. In one or more embodiments, the difference from normal operation data required to detect a deviation over a short time frame may be relatively large as compared to analysis of a longer time frame, which may require a relatively smaller difference to detect a deviation. For example, a gradual but consistent change in composition or other data over a long time frame may be used in predicting long-term failure.

The data comparison may also be made with respect to various different pieces of equipment. As one example, the data comparison may be limited to only the equipment or component in question. As another example, the data comparison may be made relative to other equipment or components in the system, and potentially to all other equipment and/or components of the same type within the system. As a further example, the data comparison may be made relative to historical data, including historical data for the same equipment or component, or historical data for other equipment or components. It is understood that data analysis does not necessarily need to be done for the purpose of detecting deviations, as described in greater detail below. For example, data comparison that indicates consistency with historical data for a piece of equipment or a component that failed in a specific way may be valuable in predicting whether and when condensation or component (e.g., seal) failure will occur.

The data used for the comparison may also depend on the stage of operation of the equipment or component. For example, start-up or shut-down of the machine may place increased stresses on the system and may require different data comparison. Different criteria (% or threshold) for deviation from normal operation may be applied during start-up or shut-down. Different comparison data may be used for analysis during start-up or shut-down as well, such as comparison to other start-up or shut-down data, rather than data from steady operation. As another example, different criteria and/or comparison data may be used during particular environmental conditions, such as based on a particular season or weather phenomenon.

In another example, the data can be compared to previous trend or pattern data from the same or other equipment or components. In this example, the overall trend or pattern of the data for the equipment or component may be analyzed to determine which previous data sets match most closely to the present data. Once one or more similar trends in data are matched, the matched data sets may provide useful predictive value. For example, matched data may be valuable in predicting whether and when condensation or component (e.g., seal) failure will occur and/or which solutions may be effectively implemented to address an actual or potential issue.

When the computer system determines that a present condensation or component failure exists or predicts the future occurrence of such an issue, the computer system can take actions accordingly, including generating alerts and/or initiating corrective actions. Examples of corrective actions to avoid condensation include changing the feed source for the gas flow and/or increasing the temperature of heaters. Another example of a corrective action is to process the gas to eliminate heavy components prone to condensation before the gas enters the heater or the compressor. This action requires the addition of a device upstream of the heater and/or the compressor to decrease the gas pressure to condense the heavy components and then re-pressurizes the remainder of the gas flow.

Gas Quality for Reciprocating Compressors

In one or more embodiments, sensors at various locations within the system can be used to detect or predict the presence of condensation or other liquid in a machine or component thereof. The computer system may take actions, such as generating alerts or initiating corrective action, based on the determined present or predicted condensation. This may be implemented in one or more embodiments to detect or predict condensation or other liquid in a cylinder of a reciprocating compressor.

In one or more embodiments, the presence of liquid, such as condensation or lubricating oil, in the cylinder of a reciprocating compressor may cause vibrations (physical and/or sonic) and/or a response in the real time P-V data of the compressor. Real-time P-V data (which may be displayed in the form of a diagram/graph) may be generated using a pressure sensor installed, e.g., on the head end and the crank end of each cylinder, and additional information, such as crank angle sensor data, compressor speed sensor data, and equipment geometry such as cylinder bore diameter, stroke length, piston rod diameter, and volumetric efficiency may be required for this calculation. Vibration and/or sound sensors may be positioned at each cylinder 331 of the reciprocating compressor 330, to detect the amplitude and frequency of these vibrations. This data can then be compared to normal operating characteristics to determine present or predicted liquid presence in the cylinder. The data collected by the vibration sensors may be processed and analyzed by the computer system in order to detect present liquid presence, predict future liquid presence, and/or initiate alerts or corrective actions related to such issues.

Sensor information may be gathered by one or more sensors and transmitted to data collection platform. Data collection platform may transmit the collected sensor data to data analysis platform, which may be at a plant or remote from a plant (e.g., in the cloud). Data analysis platform may analyze the received sensor data. Data analysis platform may compare the sensor data to one or more rules to determine if any of the issues disclosed herein are occurring. For example, condensation in a cylinder of a reciprocating compressor may be indicated if in one or more conditions are met: (1) detecting inappropriate amplitude or frequency of vibrations by sensors positioned at the cylinder, and/or (2) cooling water temperature is more than 10° F. less than the inlet gas temperature. Furthermore, data analysis platform may compare current sensor data to past sensor data from the rotating equipment, from other rotating equipment at the same plant, from other rotating equipment at other plants, from a manufacturer, or the like. Data analysis platform may determine if one or more data characteristics of the sensor data match data that may indicate any of the issues disclosed herein.

Data analysis platform may further run process simulations to suggest changes to operating parameters of the rotating equipment and associated components to avoid or limit further damage by one or more of the issues disclosed herein. In some aspects, data analysis platform may communicate with one or more vendors regarding the results of the simulation, and receive recommendations from the vendor on how to change or optimize parameters (e.g., geometry) of the equipment. Data analysis platform may use this information to create or expand a searchable database.

In one or more embodiments, the vibration data may be compared to current or archived data for the same equipment or component, and the computer system can analyze the data to make useful determinations, such as whether the data indicates that a potential liquid presence exists or will exist and/or making predictions regarding future operation. Corrective actions can be taken if deviations are determined to exist, and if such deviations are determined to be potentially indicative of an issue. The data comparison may be made across a variety of time frames, from a single cycle, to a time frame of a few minutes or hours, to real-time continuous comparison, to historical comparison over a period of months or more, and may include absolute and proportional comparisons. As one example, a deviation may be detected if the vibration data is found to differ by a set percentage (e.g., +/−5% or 10%) from normal operation data. As another example, a deviation may be detected if the vibration data exceeds a specific absolute threshold, either as a set threshold or as a set absolute difference from normal operation data. The data analysis may be done over one or more different time frames, and the deviation percentage or threshold may depend on the time frame for comparison. In one or more embodiments, the difference from normal operation data required to detect a deviation over a short time frame may be relatively large as compared to analysis of a longer time frame, which may require a relatively smaller difference to detect a deviation. For example, a gradual but consistent increase or decrease in a vibration over a long time frame may be used in predicting long-term failure.

The data comparison may also be made with respect to various different pieces of equipment. As one example, the data comparison may be limited to only the equipment or component in question. As another example, the data comparison may be made relative to other equipment or components in the system, and potentially to all other equipment and/or components of the same type within the system. As a further example, the data comparison may be made relative to historical data, including historical data for the same equipment or component, or historical data for other equipment or components. It is understood that data analysis does not necessarily need to be done for the purpose of detecting deviations, as described in greater detail below. For example, data comparison that indicates consistency with historical vibration data for a piece of equipment or a component that failed in a specific way may be valuable in predicting whether and when unacceptable liquid presence and/or resultant failure will occur.

The vibration data used for the comparison may also depend on the stage of operation of the equipment or component. For example, start-up or shut-down of the machine may place increased stresses on the system and may require different data comparison. Different criteria (% or threshold) for deviation from normal operation may be applied during start-up or shut-down. Different comparison data may be used for analysis during start-up or shut-down as well, such as comparison to other start-up or shut-down data, rather than data from steady operation. As another example, different criteria and/or comparison data may be used during particular environmental conditions, such as based on a particular season or weather phenomenon.

In another example, the vibration data can be compared to previous trend or pattern data from the same or other equipment or components. In this example, the overall trend or pattern of the vibration data for the equipment or component may be analyzed to determine which previous data sets match most closely to the present data. Once one or more similar trends in data are matched, the matched data sets may provide useful predictive value. For example, matched data may be valuable in predicting whether and when unacceptable liquid presence and/or resultant failure will occur and/or which solutions may be effectively implemented to address an actual or potential issue.

Based on the analysis and comparison of data described herein, the computer system may take various actions, including corrective actions, notifications, predictions, etc. Corrective actions may include actions to correct a present issue or prophylactic actions to address predicted future issues. For example, the system can recommend and/or initiate alternative processes to preserve the life of the equipment or component. As another example, the system can predict a failure date for the equipment or component so that corrective actions can be taken, e.g., an alternate article of equipment or component can be prepared and then can be placed on-line.

In another example, temperature sensors are used to measure the temperature of the cylinder jacket cooling water (with glycol mix) for cooling the cylinder of the compressor and the temperature of the inlet gas. If the cooling water temperature is more than 10° F. less than the inlet gas temperature, condensation from the gas flow may result in the compressor. The computer system compares these two temperatures and takes corrective action if necessary to address this issue. This may be accomplished by transmitting a signal to the cooling water temperature control machine to control the water to the proper temperature, e.g., by adjusting the valve controlling the cooling water temperature. Various different temperature sensors may be used in this configuration, including thermocouples or thermography, or any other temperature sensor described herein.

Using Molecular Weight and Invariant Mapping to Analyze Plant Production

In one or more embodiments, sensors at various locations within the system can be used to detect or infer the molecular weight of the gas flow at a particular point in the system, as well as other operational parameters indicative of performance of a particular machine or the plant as a whole. The computer system may take actions, such as generating alerts or recommendations, initiating corrective action, based on analysis of the various parameters.

In one or more embodiments, pressure, flow (Q), temperature, speed (N), inlet guide vane angle (Θ), and power sensors may be used in connection with a centrifugal compressor 310 as shown in FIG. 2, and this data can be analyzed for performance evaluation and also used to infer the molecular weight of the gas flow within the compressor. FIG. 10I illustrates a centrifugal compressor 310 provided with sensors for this purpose. Pressure and flow sensors 370, 371 may be placed upstream and downstream of the compressor 310, and the ΔP and flow are indicative of the performance of the compressor 310. Temperature sensors 372 can also be placed upstream and downstream of the compressor 310. Inlet guide vane angle sensors (not shown) may also be used in connection with this embodiment. A power sensor 373 can be positioned and configured to measure torque on the shaft of the centrifugal compressor 310, in order to directly measure power of the motor. A speed sensor may be positioned in a similar location as the torque meter 373. The P, Q, T, N, and power data can be used for performance evaluation of the compressor itself. Additionally, the molecular weight of the gas flow can be calculated from the P, Q, T, N, and power of the compressor. The following equations can be used for this purpose, and it is noted that specific heat ratio, k and gas compressibility can be generated by data analysis platform:

BHP=Torque*RPM/Torque

BHP=({dot over (m)}*ΔP)/C*η _(p)

ΔP=P _(d) −P _(s)

CPR=P _(d) /P _(s)

TDH=1545/MW*T _(s) *Z _(avg)*[(CPR)^(n)−1]/n

n=(k−1)/k*η _(p)

This technique permits real-time calculation of the molecular weight of the gas flow. The molecular weight of the gas flow will affect the performance of the compressor, and this data can therefore be used in evaluating performance of the compressor. The molecular weight of the gas flow can also be indicative of the performance of other components in the plant and/or the plant as a whole. Alternately, molecular weight can be calculated using a gas chromatograph, although this may not enable real-time calculation of molecular weight. Other properties may be measured and used in the analysis as well, including flow rate.

Various conclusions can be drawn from the molecular weight data and other data gathered by the sensors. For example, the component from which the data is taken (a centrifugal compressor in the example) may be evaluated for performance, including efficiency or operating cost, effectiveness or production capacity, and reliability or potential failure concerns. The difference between the current performance and the desired performance can be quantified as an absolute or proportional difference. The production can be analyzed and compared to other data, including thresholds or normal operational data, and the computer system may take action based on such analysis or comparison. This difference can also be quantified monetarily, and this monetary determination can then be used to determine whether cost justification for part replacement exists. Corrective action may be taken by the computer system in the form of recommending part replacement, scheduling replacement, and/or ordering replacement parts, if warranted based on the cost data. Similar determinations may be made with respect to other equipment, using the same or similar data to calculate molecular weight and evaluate performance.

In another example, the molecular weight calculated from a single piece of equipment or from multiple pieces of equipment may be evaluated to assess the performance of other equipment upstream from the measurement point, or the performance of the plant as a whole. As described above, the difference between the current performance and the desired performance can be quantified as an absolute or proportional difference. The production can be analyzed and compared to other data, including thresholds or normal operational data, and the computer system may take action based on such analysis or comparison. This difference can also be quantified monetarily, and this monetary determination can then be used as a basis for future actions.

The data collected by the sensors and the calculated molecular weight data described above may be processed and analyzed by the computer system in order to detect present issues, predict future issues, evaluate and quantify performance, and/or initiate alerts or corrective actions related to such issues as described above. Sensor information may be gathered by one or more sensors and transmitted to data collection platform. Data collection platform may transmit the collected sensor data to data analysis platform, which may be at a plant or remote from a plant (e.g., in the cloud). Data analysis platform may analyze the received sensor data. Data analysis platform may compare the sensor data to one or more rules to determine if any of the issues disclosed herein are occurring. For example, poor performance of a compressor may be indicated if one or more conditions are met: (1) measuring the ΔP pressure of the compressor is outside an appropriate range of values, (2) measuring the ΔV volume of the compressor is outside an appropriate range of values, (3) the temperature of the inlet and outlet gas (or ΔT) is outside an appropriate range of values, and/or (4) the power of the compressor is outside an appropriate range of values. Furthermore, data analysis platform may compare current sensor data to past sensor data from the rotating equipment, from other rotating equipment at the same plant, from other rotating equipment at other plants, from a manufacturer, or the like. Data analysis platform may determine if one or more data characteristics of the sensor data match data that may indicate any of the issues disclosed herein.

Data analysis platform may further run process simulations to suggest changes to operating parameters of the rotating equipment and associated components to avoid or limit further damage by one or more of the issues disclosed herein. In some aspects, data analysis platform may communicate with one or more vendors regarding the results of the simulation, and receive recommendations from the vendor on how to change or optimize parameters (e.g., geometry) of the equipment. Data analysis platform may use this information to create or expand a searchable database.

In one or more embodiments, the collected data may be compared to current or archived data for the same equipment or component, and the computer system can analyze the data to make useful determinations, such as whether the data indicates that a potential issue exists or will exist and/or making predictions regarding future operation. Corrective actions can be taken if deviations are determined to exist, and if such deviations are determined to be potentially indicative of an issue. The data comparison may be made across a variety of time frames, from a single cycle, to a time frame of a few minutes or hours, to real-time continuous comparison, to historical comparison over a period of months or more, and may include absolute and proportional comparisons. As one example, a deviation may be detected if a particular parameter is found to differ by a set percentage (e.g., +/−5% or 10%) from normal operation data. As another example, a deviation may be detected if a particular parameter exceeds a specific absolute threshold, either as a set threshold or as a set absolute difference from normal operation data. The data analysis may be done over one or more different time frames, and the deviation percentage or threshold may depend on the time frame for comparison. In one or more embodiments, the difference from normal operation data required to detect a deviation over a short time frame may be relatively large as compared to analysis of a longer time frame, which may require a relatively smaller difference to detect a deviation. For example, a gradual but consistent increase or decrease in a particular measurement over a long time frame may be used in predicting long-term failure or production issues.

The data comparison may also be made with respect to various different pieces of equipment. As one example, the data comparison may be limited to only the equipment or component in question. As another example, the data comparison may be made relative to other equipment or components in the system, and potentially to all other equipment and/or components of the same type within the system. As a further example, the data comparison may be made relative to historical data, including historical data for the same equipment or component, or historical data for other equipment or components. It is understood that data analysis does not necessarily need to be done for the purpose of detecting deviations, as described in greater detail below. For example, data comparison that indicates consistency with historical data for a piece of equipment or a component that failed in a specific way or had particular performance characteristics may be valuable in predicting future performance, including potentially whether and when failure will occur.

The data used for the comparison may also depend on the stage of operation of the equipment or component. For example, start-up or shut-down of the machine may place increased stresses on the system and may require different data comparison. Different criteria (% or threshold) for deviation from normal operation may be applied during start-up or shut-down. Different comparison data may be used for analysis during start-up or shut-down as well, such as comparison to other start-up or shut-down data, rather than data from steady operation. As another example, different criteria and/or comparison data may be used during particular environmental conditions, such as based on a particular season or weather phenomenon.

In another example, the data can be compared to previous trend or pattern data from the same or other equipment or components. In this example, the overall trend or pattern of the data for the equipment or component may be analyzed to determine which previous data sets match most closely to the present data. Once one or more similar trends in data are matched, the matched data sets may provide useful predictive value. For example, matched data may be valuable in predicting future performance (including whether and when failure will occur), reasonable expectations for present performance, and/or which solutions may be effectively implemented to address an actual or potential issue.

Based on the analysis and comparison of data described herein, the computer system may take various actions, including corrective actions, notifications, predictions, etc. Corrective actions may include actions to correct a present issue or prophylactic actions to address predicted future issues. For example, the system can recommend and/or initiate alternative processes to preserve the life of the equipment or component. Examples of alternative processes that can be implemented are given above and will depend on the equipment or component and the potential issue detected or predicted. As another example, the system can predict a failure date for the equipment or component so that corrective actions can be taken, e.g., an alternate article of equipment or component can be prepared and then can be placed on-line. As a further example, the system can predict a replacement date by projecting when the cost of replacement for the equipment or component will be justified monetarily due to decreased performance, so that corrective actions can be taken, e.g., an alternate article of equipment or component can be prepared and then can be placed on-line.

The data taken from one or more of the various sensors may be correlated with weather and environmental data to determine predictive models of potential problems in the current rotating equipment, and/or other rotating equipment used in different processes and environments. The data may be measured on a periodic basis. In some embodiments, more data points may enable better predictive outcome (e.g., allowing early prediction of potential failures and/or implementation of preventative measures).

CONCLUSION

Aspects of the disclosure have been described in terms of illustrative embodiments thereof. Numerous other embodiments, modifications, and variations within the scope and spirit of the appended claims will occur to persons of ordinary skill in the art from a review of this disclosure. For example, one or more of the steps illustrated in the illustrative figures may be performed in other than the recited order, and one or more depicted steps may be optional in accordance with aspects of the disclosure. 

What is claimed is:
 1. A system comprising: a reactor; a heater; a condenser; a rotating equipment; a valve associated with the rotating equipment; one or more sensors associated with the rotating equipment; a data collection platform comprising: one or more processors of the data collection platform; a communication interface of the data collection platform; and memory storing executable instructions that, when executed, cause the data collection platform to: receive, from the one or more sensors associated with the rotating equipment, sensor data comprising operation information associated with the rotating equipment; correlate the sensor data from the one or more sensors with metadata comprising time data, the time data corresponding to the operation information associated with the rotating equipment; and transmit the sensor data; and a data analysis platform, comprising: one or more processors of the data analysis platform; a communication interface of the data analysis platform; and memory storing executable instructions that, when executed, cause the data analysis platform to: receive, from the data collection platform, the sensor data comprising the operation information associated with the rotating equipment; analyze the sensor data comprising the operation information associated with the rotating equipment; based on analyzing the sensor data comprising the operation information associated with the rotating equipment, determine an operating condition of the rotating equipment; based on the operating condition of the rotating equipment, determine an adjustment to the operating condition of the rotating equipment; and send a command configured to cause the adjustment to the operating condition of the rotating equipment by causing adjustment to the valve associated with the rotating equipment.
 2. The system of claim 1, wherein the memory of the data collection platform stores executable instructions that, when executed, cause the data collection platform to: receive the time data corresponding to the operation information associated with rotating equipment, the time data comprising a start time corresponding to the operation information associated with the rotating equipment and a stop time corresponding to the operation information associated with the rotating equipment.
 3. The system of claim 1, wherein the memory of the data analysis platform stores executable instructions that, when executed, cause the data analysis platform to: receive weather information corresponding to weather at a geographic location of the rotating equipment; correlate the sensor data from the one or more sensors with the weather information corresponding to weather at the geographic location of the rotating equipment; and determine whether the operating condition of the rotating equipment is associated with the weather at the geographic location of the rotating equipment.
 4. The system of claim 1, wherein the memory of the data analysis platform stores executable instructions that, when executed, cause the data analysis platform to: send an alert to a remote device, the alert comprising the operating condition of the rotating equipment.
 5. The system of claim 1, wherein the memory of the data analysis platform stores executable instructions that, when executed, cause the data analysis platform to: send a command configured to cause adjustment of a hydraulic pressure associated with the rotating equipment by an adjustment to the valve associated with the rotating equipment.
 6. The system of claim 1, wherein the rotating equipment is a compressor.
 7. The system of claim 1, wherein the rotating equipment is a pump.
 8. The system of claim 1, wherein the rotating equipment is a turbine.
 9. The system of claim 1, wherein the one or more sensors associated with the rotating equipment comprise a temperature sensor.
 10. The system of claim 1, wherein the one or more sensors associated with the rotating equipment comprise a pressure sensor.
 11. A method of operation for a plant comprising a reactor, a heater, a condenser, a rotating equipment, and a valve associated with the rotating equipment, the method comprising: at a data analysis platform comprising one or more processors, memory, and a communication interface in communication with one or more sensors associated with the rotating equipment: receiving sensor data comprising operation information associated with the rotating equipment; analyzing the sensor data comprising the operation information associated with the rotating equipment; based on analyzing the sensor data comprising the operation information associated with the rotating equipment, determining an operating condition of the rotating equipment; based on the operating condition of the rotating equipment, determining an adjustment to the operating condition of the rotating equipment; and sending a command configured to cause the adjustment to the operating condition of the rotating equipment by causing adjustment to the valve associated with the rotating equipment.
 12. The method of claim 11, comprising: receiving time data corresponding to the operation information associated with rotating equipment, the time data comprising a start time corresponding to the operation information associated with the rotating equipment and a stop time corresponding to the operation information associated with the rotating equipment.
 13. The method of claim 11, comprising: receiving weather information corresponding to weather at a geographic location of the rotating equipment; correlating the sensor data from the one or more sensors with the weather information corresponding to weather at the geographic location of the rotating equipment; and determining whether the operating condition of the rotating equipment is associated with the weather at the geographic location of the rotating equipment.
 14. The method of claim 11, comprising: sending an alert to a remote device, the alert comprising the operating condition of the rotating equipment.
 15. The method of claim 11, comprising: sending a command configured to cause adjustment of a hydraulic pressure associated with the rotating equipment by an adjustment to the valve associated with the rotating equipment.
 16. A method of operation for a refinery comprising a reactor, a heater, a condenser, a rotating equipment, and a valve associated with the rotating equipment, the method comprising: at a data analysis platform comprising one or more processors, memory, and a communication interface in communication with one or more sensors associated with the rotating equipment: receiving sensor data comprising operation information associated with the rotating equipment; analyzing the sensor data comprising the operation information associated with the rotating equipment; based on analyzing the sensor data comprising the operation information associated with the rotating equipment, determining an operating condition of the rotating equipment; based on the operating condition of the rotating equipment, determining an adjustment to the operating condition of the rotating equipment; and sending a command configured to cause the adjustment to the operating condition of the rotating equipment by causing adjustment to the valve associated with the rotating equipment.
 17. The method of claim 16, comprising: receiving time data corresponding to the operation information associated with rotating equipment, the time data comprising a start time corresponding to the operation information associated with the rotating equipment and a stop time corresponding to the operation information associated with the rotating equipment.
 18. The method of claim 16, comprising: receiving weather information corresponding to weather at a geographic location of the rotating equipment; correlating the sensor data from the one or more sensors with the weather information corresponding to weather at the geographic location of the rotating equipment; and determining whether the operating condition of the rotating equipment is associated with the weather at the geographic location of the rotating equipment.
 19. The method of claim 16, comprising: sending an alert to a remote device, the alert comprising the operating condition of the rotating equipment.
 20. The method of claim 16, comprising: sending a command configured to cause adjustment of a hydraulic pressure associated with the rotating equipment by an adjustment to the valve associated with the rotating equipment. 